Dermatological conditions affect a significant portion of the global population, with delayed diagnoses often leading to worsening conditions. This project introduces an AI-based web application that uses deep learning algorithms and artificial neural networks to analyse skin images and provide rapid preliminary diagnoses. The system can identify various dermatological conditions, assess severity, and offer accuracy metrics, thereby facilitating early detection and improving healthcare outcomes. This tool addresses the growing gap between patients and dermatological care, especially in areas with limited (...) specialist availability. (shrink)

Abstract: Tomatoes are among the most popular vegetables in the world due to their frequent use in many dishes, which fall into many varieties in common and traditional foods, and due to their rich ingredients such as vitamins and minerals, so they are frequently used on a daily basis, When we focus our attention on this vegetable, we must also focus and take into consideration the diseases that affect this vegetable, a deep learning model that classifies tomato diseases has been (...) proposed. The aim of this paper is to diagnose tomato diseases based on a dataset containing 11000 picture and 11 classes. The model gave the final accuracy test with Accuracy (99.87%), F1-score (99.87%), Recall (99.87%), and Precision (99.88%) in an estimated time 1.36 second, as with these results it proved its effectiveness and good ability in the classification of the testing data. (shrink)

In this article, I will reconstruct the monocausal model and argue that modern 'multifactorial diseases' are not monocausal by definition. 'Multifactorial diseases' are instead defined according to a constitutive disease model. On closer analysis, infectious diseases are also defined using the constitutive model rather than the monocausal model. As a result, our classification models alone cannot explain why infectious diseases have a universal etiology while chronic and noncommunicable diseases lack one. The explanation is instead provided by the nineteenth-century (...) germ theorists. (shrink)

Brain-related diseases are among the most difficult diseases due to their sensitivity, the difficulty of performing operations, and their high costs. In contrast, the operation is not necessary to succeed, as the results of the operation may be unsuccessful. One of the most common diseases that affect the brain is Alzheimer’s disease, which affects adults, a disease that leads to memory loss and forgetting information in varying degrees. According to the condition of each patient. For these reasons, it (...) is important to classify memory loss and to know the patient at what level and his assessment of Alzheimer's disease through CT scans of the brain. In this thesis, we review ways and techniques to use deep learning classification to classifying the Alzheimer's Disease The proposed method used to improve patient care, reduce costs, and allow fast and reliable analysis in large studies. The model will be designed using Python language for implementing the system, which is very useful for doctors, classifying the Alzheimer's Disease, was used. The model used 70% from image for training and 30% from image for validation, our trained model achieved an accuracy of 100% on a held-out test set. (shrink)

„Errors are the greatest obstacles to the progress of science; to correct such errors is of more practical value than to achieve new knowledge,“ asserted Eugen Bleuler. Basic error of several prevailing classification schemes of pathological conditions, as for example ICD-10, lies in confusing and mixing symptoms with diseases, what makes them unscientific. Considering the need to bring order into the chaos and light into terminological obscureness, I introduce the Causal classification of diseases originating from the notion of (...) bodily wholeness and causality of its loss. (shrink)

Abstract: Alzheimer's disease (AD) is one of the most common types of dementia. Symptoms appear gradually and end with severe brain damage. People with Alzheimer's disease lose the abilities of knowledge, memory, language and learning. Recently, the classification and diagnosis of diseases using deep learning has emerged as an active topic covering a wide range of applications. This paper proposes examining abnormalities in brain structures and detecting cases of Alzheimer's disease especially in the early stages, using (...) features derived from medical images. The entire brain image was passed on through the transmission of Xception learning architectures. The Convolutional Neural Network (CNN) was constructed with the help of separable convolution layers that It can automatically learn general features from imaging data for classification. (shrink)

Abstract: In recent years, the outbreak of various poultry diseases has posed a significant threat to the global poultry industry. Therefore, the accurate and timely detection of chicken diseases is critical to reduce economic losses and prevent the spread of diseases. In this study, we propose a method for classifying chicken diseases using a convolutional neural network (CNN). The proposed method involves preprocessing the chicken images, building and training a CNN model, and evaluating the performance of the model. The dataset (...) used in this study comprises chicken images of three different diseases: Newcastle disease, avian influenza, and infectious bursal disease. The proposed method achieved an overall F1-score of 99.04% in classifying the chicken diseases. The results demonstrate the effectiveness of using CNN in classifying chicken diseases and provide a promising approach for early detection and diagnosis of poultry diseases. (shrink)

Abstract: In this study, we explore the challenge of identifying and preventing diseases in apple trees, which is a popular activity but can be difficult due to the susceptibility of these trees to various diseases. To address this challenge, we propose the use of Convolutional Neural Networks, which have proven effective in automatically detecting plant diseases. To validate our approach, we use images of apple leaves, including Apple Rot Leaves, Leaf Blotch, Healthy Leaves, and Scab Leaves collected from Kaggle which (...) is part from the Plant Village dataset. We generate a comprehensive training dataset using techniques such as image filtering, compression, and generation. Our model achieves impressive accuracy scores for all classes, with an overall accuracy of 99.93% on a dataset of 10,000 labeled images . (shrink)

Identifying the necessary and sufficient conditions for individuating and classifying diseases is a matter of great importance in the fields of law, ethics, epidemiology, and of course, medicine. In this paper, I first propose a means of achieving this goal, ensuring that no two distinct disease-types could correctly be ascribed to the same disease-token. I then posit a metaphysical ontology of diseases—that is, I give an account of what a disease is. This is essential to providing the (...) most effective means of interfering with disease processes. Following existing work in the philosophy of medicine and epidemiology (primarily Christopher Boorse; Caroline Whitbeck; Alexander Broadbent), philosophy of biology (Joseph LaPorte; D.L. Hull), conditional analyses of causation (J.L. Mackie; David Lewis), and recent literature on dispositional essentialism (Stephen Mumford and Rani Anjum; Alexander Bird), I endorse a dispositional conception of disease. Following discussion of various conceptions of disease-identity, their relations to the clinical and pathological effects of the diseases in question, and how diseases are treated, I conclude (i) that diseases should be individuated by their causes, and (ii) that diseases are causal processes best seen as simultaneously acting sequences of mutually manifesting dispositions. (shrink)

Automatic detection of diseases and anatomical landmarks in medical images by the use of computers is important and considered a challenging process that could help medical diagnosis and reduce the cost and time of investigational procedures and refine health care systems all over the world. Recently, gastrointestinal (GI) tract disease diagnosis through endoscopic image classification is an active research area in the biomedical field. Several GI tract disease classification methods based on image processing and machine learning (...) techniques have been proposed by diverse research groups in the recent past. However, yet effective and comprehensive deep ensemble neural network-based classification model with high accuracy classification results is not available in the literature. In this thesis, we review ways and mechanisms to use deep learning techniques to research on multi-disease computer-aided detection about gastrointestinal and identify these images. We re-trained five state-of-the-art neural network architectures, VGG16, ResNet, MobileNet, Inception-v3, and Xception on the Kvasir dataset to classify eight categories that include an anatomical landmark (pylorus, z-line, cecum), a diseased state (esophagitis, ulcerative colitis, polyps), or a medical procedure (dyed lifted polyps, dyed resection margins) in the Gastrointestinal Tract. Our models have showed results with a promising accuracy which is a remarkable performance with respect to the state-of-the-art approaches. The resulting accuracies achieved using VGG, ResNet, MobileNet, Inception-v3, and Xception were 98.3%, 92.3%, 97.6%, 90% and 98.2%, respectively. As it appears, the most accurate result has been achieved when retraining VGG16 and Xception neural networks with accuracy reache to 98% due to its high performance on training on ImageNet dataset and internal structure that support classification problems. (shrink)

Abstract: Potatoes are edible tubers, available worldwide and all year long. They are relatively cheap to grow, rich in nutrients, and they can make a delicious treat. The humble potato has fallen in popularity in recent years, due to the interest in low-carb foods. However, the fiber, vitamins, minerals, and phytochemicals it provides can help ward off disease and benefit human health. They are an important staple food in many countries around the world. There are an estimated 200 varieties (...) of potatoes, which can be classified into a number of categories based on the cooked texture and ingredient functionality. Using a public dataset of 2400 images of potatoes, we trained a deep convolutional neural network to identify 4 types (Red, Red Washed, Sweet, and White).The trained model achieved an accuracy of 99.5% of test set, demonstrating the feasibility of this approach. (shrink)

Debates about the genuine disease status of controversial diseases rely on intuitions about a range of factors. Adopting tools from experimental philosophy, this paper explores some of the factors that influence judgments about whether low sexual desire should be considered a disease and whether it should be medically treated. Drawing in part on some assumptions underpinning a divide in the literature between viewing low sexual desire as a genuine disease and seeing it as improperly medicalized, we investigate (...) whether health and disease judgments are affected by factors such as an individual’s gender, the cause of the low desire, whether the desire is high or low, and both personal and societal valuations of the condition. Our main findings indicate that (a) the cause of a condition influences whether it is judged a disorder, (b) how the individual values the condition influences whether the condition is seen as a proper target of medical intervention, and (c) perceived dysfunction influences judgments regarding health, disorder classification, medicalization, and medical intervention. Our findings help further illuminate the intricate interplay of factors that influence judgments about health and disease in controversial conditions. (shrink)

Abstract: Tomatoes are part of the major crops in food security. Tomatoes are plants grown in temperate and hot regions of South American origin from Peru, and then spread to most countries of the world. Tomatoes contain a lot of vitamin C and mineral salts, and are recommended for people with constipation, diabetes and patients with heart and body diseases. Studies and scientific studies have proven the importance of eating tomato juice in reducing the activity of platelets in diabetics, which (...) helps in protecting them from developing deadly blood clots. A tomato classification approach is presented with a data set containing approximately 5,266 images with 7 species belonging to tomatoes. The Neural Network Algorithms (CNN), a deep learning technique applied widely in image recognition, is used for this task. (shrink)

Cardiovascular diseases, including heart disease, pose a significant global health challenge, contributing to a substantial burden on healthcare systems and individuals. Early detection and accurate prediction of heart disease are crucial for timely intervention and improved patient outcomes. This research explores the potential of neural networks in predicting heart disease using a dataset collected from Kaggle, consisting of 1025 samples with 14 distinct features. The study's primary objective is to develop an effective neural network model for binary (...) classification, identifying the presence or absence of heart disease. The neural network architecture includes an input layer, a hidden layer, and an output layer, designed to capture intricate relationships within the dataset. Rigorous training and validation processes, accompanied by data preprocessing steps, ensure the model's robustness and generalization capabilities. The results demonstrate promising performance, with an accuracy of 92% and an average error of 0.062. Moreover, an analysis of feature importance highlights key predictors, including "oldpeak," "thalach," "trestbps," "ca," "thal," "cp," "chol," "sex," "restecg," "age," "slope," "fbs," and "exang." This research contributes to the field of predictive healthcare by leveraging neural networks to enhance heart disease prediction. The developed model offers the potential for early identification of individuals at risk, facilitating timely medical interventions and ultimately improving public health. Further exploration of machine learning techniques in healthcare promises to reshape disease prediction and prevention strategies. (shrink)

There are many eye diseases but the most two common retinal diseases are Age-Related Macular Degeneration (AMD), which the sharp, central vision and a leading cause of vision loss among people age 50 and older, there are two types of AMD are wet AMD and DRUSEN. Diabetic Macular Edema (DME), which is a complication of diabetes caused by fluid accumulation in the macula that can affect the fovea. If it is left untreated it may cause vision loss. Therefore, early detection (...) of diseases is a critical importance. Our main goal is to help doctors detect these diseases quickly before reaching a late stage of the disease. In ophthalmology, optical coherence tomography (OCT) is critical for evaluating retinal conditions. OCT is an imaging technique used to capture high-resolution cross-sections of the retinas of patient. In this thesis, we review ways and techniques to use deep learning classification of the optical coherence tomography images of diseases from which a Retinal is suffering. The models used to improve patient care are (VGG-16, MobileNet, ResNet-50, Inception V3, and Xception) to reduce costs and allow fast and reliable analysis in large studies. The obtained results are encouraging, since the best model ResNet-50 reaching 96.21% of testing accuracy, which is very useful for doctors, to diagnose retinal diseases. (shrink)

Abstract: Vegetables are an essential component of a healthy diet and play a critical role in promoting overall health and well- being. Vegetables are rich in important vitamins and minerals, including vitamin C, folate, potassium, and iron. They also provide fiber, which helps maintain digestive health and prevent chronic diseases. We are proposing a deep learning model for the classification of vegetables. A dataset was collected from Kaggle depository for Vegetable with 15000 images for 15 different classes. The data (...) was preprocessed, normalized and split into three sets (train, valid, test). The proposed model was trained and validated using the train and valid sets and accuracy of both training and validation was very high. For the evaluation of the proposed model we utilized these metrics: accuracy, F1-score, precision, Recall and time required for testing. We then tested the proposed model using the test set. The result of the testing was accuracy (99.95%), F1-score (99.95%), precision (99.95%), Recall (99.95%) and time required for testing the test set was 1.38 seconds. (shrink)

In this chapter, I provide an overview of phenomenological approaches to psychiatric classification. My aim is to encourage and facilitate philosophical debate over the best ways to classify psychiatric disorders. First, I articulate phenomenological critiques of the dominant approach to classification and diagnosis—i.e., the operational approach employed in the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) and the International Classification of Diseases (ICD-10). Second, I describe the type or typification approach to psychiatric classification, which I (...) distinguish into three different versions: ideal types, essential types, and prototypes. I argue that despite their occasional conflation in the contemporary literature, there are important distinctions among these approaches. Third, I outline a new phenomenological-dimensional approach. I show how this approach, which starts from basic dimensions of human existence, allows us to investigate the full range of psychopathological conditions without accepting the validity of current diagnostic categories. (shrink)

While classifications of mental disorders have existed for over one hundred years, it still remains unspecified what terms such as 'mental disorder', 'disease' and 'illness' might actually denote. While ontologies have been called in aid to address this shortfall since the GALEN project of the early 1990s, most attempts thus far have sought to provide a formal description of the structure of some pre-existing terminology or classification, rather than of the corresponding structures and processes on the side of (...) the patient. We here present a view of mental disease that is based on ontological realism and which follows the principles embodied in Basic Formal Ontology and in the application of BFO in the Ontology of General Medical Science. We analyzed statements about what counts as a mental disease provided in the research agenda for the DSM-V, and in Pies' model. The results were used to assess whether the representational units of BFO and OGMS were adequate as foundations for a formal representation of the entities in reality that these statements attempt to describe. We then analyzed the representational units specific to mental disease and provided corresponding definitions. Our key contributions lie in the identification of confusions and conflations in the existing terminology of mental disease and in providing what we believe is a framework for the sort of clear and unambiguous reference to entities on the side of the patient that is needed in order to avoid these confusions in the future. (shrink)

Biomedical research is increasingly a matter of the navigation through large computerized information resources deriving from functional genomics or from the biochemistry of disease pathways. To make such navigation possible, controlled vocabularies are needed in terms of which data from different sources can be unified. One of the most influential developments in this regard is the so-called Gene Ontology, which consists of controlled vocabularies of terms used by biologists to describe cellular constituents, biological processes and molecular functions, organized into (...) hierarchies via the relation of class subsumption. Here we seek to provide a rigorous account of the logic of classification that underlies GO and similar biomedical ontologies. Drawing on Aristotle, we develop a system of axioms and definitions for the treatment of biological classes and instances. (shrink)

Background: Dengue Disease is a mosquito-borne tropical disease caused by the dengue virus, symptoms typically begin three to fourteen days after infection. This may include a high fever, headache, vomiting, muscle and joint pains, and a characteristic skin rash. Dengue serology is applied in different settings, such as for surveillance, in health care facilities in endemic areas and in travel clinics in non-endemic areas. The applicability and quality of serological tests in dengue endemic regions has to be (...) judged against a background of potential cross reactivity with other flavi-viruses, difficulties in distinguishing primary from secondary infections and technological problems related to the fact that most dengue endemic regions are relatively poor of resources .Objectives: to help doctors and patients in diagnosing Dengue Disease and give them the information of how to prevent Dengue Disease and to be able to understand the signs and symptoms of Dengue Disease. Methods: We collected all relevant material for Dengue Disease. Then we designed and implemented a knowledge based system for diagnosing Dengue Disease using SL5 Object Language. Results: The knowledge based system was evaluated by a group of Patients and specialized doctors and they found it very friendly and easy to use. (shrink)

Abstract: People all over the world have been drinking tea for thousands of centuries, and for good reason. Many types of teas can help you stay healthy by boosting your immune system, reducing inflammation, and even preventing cancer and heart disease. There is sufficient material to show that regularly consuming tea can improve your health over the long term. A deep learning model that categorizes tea disorders has been completed. When focusing on the tea, we must also focus on (...) and take into account the diseases that harm it. The aim of this paper is to diagnose tea diseases based on a dataset containing 8000 picture and 8 classes .The model gave the final accuracy test with an F1-score of 99.41% in an estimated time 3 second, as with these results it proved its effectiveness and good ability in the classification test. (shrink)

The dataset is preprocessed to remove noise and augmented to address the issue of class imbalance. The CNN model is then trained, validated, and tested on this dataset. The results indicate that the deep learning model achieves a classification accuracy of over 95% for most plant diseases. Additionally, the system is designed to provide real-time feedback to farmers, helping them take immediate corrective action. This automated approach eliminates the need for expert human intervention and can be deployed on mobile (...) devices for ease of use in rural areas. The potential future applications of this system include integration with precision agriculture techniques and expansion to a wider range of crops. (shrink)

Background: Arthritis is very common but is not well understood. Actually, “arthritis” is not a single disease; it is an informal way of referring to joint pain or joint disease. There are more than 100 different types of arthritis and related conditions. People of all ages, sexes and races can and do have arthritis, and it is the leading cause of disability in America. More than 50 million adults and 300,000 children have some type of arthritis. It is (...) most common among women and occurs more frequently as people get older. Common arthritis joint symptoms include swelling, pain, stiffness and decreased range of motion. Symptoms may come and go. They can be mild, moderate or severe. They may stay about the same for years, but may progress or get worse over time. Severe arthritis can result in chronic pain, inability to do daily activities and make it difficult to walk or climb stairs. Arthritis can cause permanent joint changes. These changes may be visible, such as knobby finger joints, but often the damage can only be seen on X-ray. Some types of arthritis also affect the heart, eyes, lungs, kidneys and skin as well as the joints. Objectives: The main goal of this expert system is to get the appropriate diagnosis of disease and the correct treatment and give the appropriate method of treatment through several tips that concern the disease and how to treat it and we will see it through the application on the expert system. Methods: in this paper the design of the proposed Expert System which was produced to help Orthopedist in diagnosing Arthritis disease through its symptoms such as: pain on pressure in a joint , Inflammation indicated by joint swelling, Stiffness especially in the morning , Loss of flexibility of joint, Limited, joint movement, Deformity of the joints , Weight loss and fatigue , Non-specific fever and Crepitus. The proposed expert system presents an overview about Arthritis disease is given, the cause of diseases is outlined and the treatment of disease whenever possible is given out. SL5 Object Expert System language was used for designing and implementing the proposed expert system. Results: The proposed Arthritis disease diagnosis expert system was evaluated by Orthopedics students and they were satisfied with its performance. Conclusions: The Proposed expert system is very useful for Orthopedist, patients with arthritis and newly graduated Orthopedics students. (shrink)

In today’s world it became difficult for daily routine check-up. The Heart disease system is an end user support and online consultation project. Here the motto behind it is to make a person to know about their heart related problem and according to it formulate them how much vital the disease is. It will be easy to access and keep track of their respective health. Thus, it’s important to predict the disease as earliest. Attributes such as Bp, (...) Cholesterol, Diabetes are fed into Classification methods of Machine Learning are been used to predict risk of heart disease. (shrink)

Abstract: A corn crop typically refers to a large-scale cultivation of corn (also known as maize) for commercial purposes such as food production, animal feed, and industrial uses. Corn is one of the most widely grown crops in the world, and it is a major staple food for many cultures. Corn crops are grown in various regions of the world with different climates, soil types, and farming practices. In the United States, for example, the Midwest is known as the "Corn (...) Belt" due to its extensive corn production. Corn crops can be grown using a variety of methods, including conventional tillage, no-till farming, and the use of genetically modified crops. Due to its importance, there is a need for discovering the corn diseases and treating them. The aim of this study is to propose a deep learning model for the classification of corn leaf diseases based on Convolutional Neural Network. A model for classifying maize diseases was developed using a dataset for classifying Corn diseases that contains 4 classes of disease. The dataset was collected from Keggel website. The proposed model was trained, validated, and tested. The F1-score (99.83%), Recall (99.83%), precision (99.83%), Accuracy (99.83%). (shrink)

Abstract: This research employs a Random Forest classification model to predict and assess obesity and cardiovascular disease (CVD) risk based on a comprehensive dataset collected from individuals in Mexico, Peru, and Colombia. The dataset comprises 17 attributes, including information on eating habits, physical condition, gender, age, height, and weight. The study focuses on classifying individuals into different health risk categories using machine learning algorithms. Our Random Forest model achieved remarkable performance with an accuracy, F1-score, recall, and precision all (...) reaching 97.23%. The model's success is attributed to its ability to effectively leverage attributes related to eating habits, physical condition, and demographic factors. Feature importance analysis reveals key factors contributing to accurate predictions. This paper contributes to the field of health analytics by demonstrating the effectiveness of Random Forest in predicting and categorizing health risks. The insights gained from this research have the potential to inform personalized health interventions and contribute to the advancement of precision health strategies. Furthermore, the study highlights the importance of leveraging machine learning techniques for health risk assessment in diverse populations. (shrink)

he health of plants plays a crucial role in ensuring agricultural productivity and food security. Early detection of plant diseases can significantly reduce crop losses, leading to improved yields. This paper presents a novel approach for plant disease recognition using deep learning techniques. The proposed system automates the process of disease detection by analyzing leaf images, which are widely recognized as reliable indicators of plant health. By leveraging convolutional neural networks (CNNs), the model identifies various plant diseases with (...) high accuracy. The experimental setup includes a dataset consisting of healthy and diseased leaf images of different plant species. The dataset is preprocessed to remove noise and augmented to address the issue of class imbalance. The CNN model is then trained, validated, and tested on this dataset. (shrink)

The health of plants plays a crucial role in ensuring agricultural productivity and food security. Early detection of plant diseases can significantly reduce crop losses, leading to improved yields. This paper presents a novel approach for plant disease recognition using deep learning techniques. The proposed system automates the process of disease detection by analyzing leaf images, which are widely recognized as reliable indicators of plant health. By leveraging convolutional neural networks (CNNs), the model identifies various plant diseases with (...) high accuracy. The experimental setup includes a dataset consisting of healthy and diseased leaf images of different plant species. The dataset is preprocessed to remove noise and augmented to address the issue of class imbalance. The CNN model is then trained, validated, and tested on this dataset. (shrink)

The health of plants plays a crucial role in ensuring agricultural productivity and food security. Early detection of plant diseases can significantly reduce crop losses, leading to improved yields. This paper presents a novel approach for plant disease recognition using deep learning techniques. The proposed system automates the process of disease detection by analyzing leaf images, which are widely recognized as reliable indicators of plant health. By leveraging convolutional neural networks (CNNs), the model identifies various plant diseases with (...) high accuracy. The experimental setup includes a dataset consisting of healthy and diseased leaf images of different plant species. The dataset is preprocessed to remove noise and augmented to address the issue of class imbalance. The CNN model is then trained, validated, and tested on this dataset. The results indicate that the deep learning model achieves a classification accuracy of over 95% for most plant diseases. A. (shrink)

Coronary Heart Disease (CHD) is one of the leading causes of death nowadays. Prediction of the disease at an early stage is crucial for many health care providers to protect their patients and save lives and costly hospitalization resources. The use of machine learning in the prediction of serious disease events using routine medical records has been successful in recent years. In this paper, a comparative analysis of different machine learning techniques that can accurately predict the occurrence (...) of CHD events from clinical data was performed. Four machine learning classifiers, namely Logistic Regression, Support Vector Machine (SVM), K- Nearest Neighbor (KNN), and Multi-Layer Perceptron (MLP) Neural Networks were identified and applied to a dataset of 462 medical instances and 9 features as well as the class feature from the South African Heart Disease data retrieved from the KEEL repository. The dataset consists of 302 records of healthy patients and 160 records of patients who suffer from CHD. In order to handle the imbalanced classification problem, the K-means algorithm along with Synthetic Minority Oversampling TEchnique (SMOTE) was used in this study. The empirical results of applying the four machine learning classifiers on the oversampled dataset have been very promising. The results reported using different evaluation metrics showed that SVM has achieved the highest overall prediction performance. (shrink)

The first part of the essay examines the different premises, of Aristotelian and Galenic origin, for the idea of an inherent consumption of the natural heat of every living body, discussing the contributions of Isaac Israeli, Avicenna and Averroes to the reflection on the relationship between the secondary humours (or moistures) and the peculiar category of fevers called ‘hectic’. The second part of the article discusses how the link between moisture, heat and food was taken up and elaborated by Latin (...) Scholastic masters (in particular Thomas Aquinas), and considers the use of the notion of ‘radical moisture’ in the field of theology. Aquinas does not follow the previous theological tradition, originating with Peter Lombard, which denies the possibility that the matter assimilated through food is part of the bodily core that belongs to every human being from birth. The moisture provided by food is, in fact, inseparable from the (radical) moisture that originally belongs to each individual; as such, it is part of the ‘truth of human nature’ (veritas humanae naturae) that will be restored at the moment of the resurrection of the body. (shrink)

Agricultural productivity is something on which Indian economy highly depends. This is the one of the reasons that disease detection in plants plays a vital role in agriculture field, as having disease in plants are unavoidable. If proper care is not taken in this area, then it causes serious effects on plants and due to which the overall agriculture yield will be affected. For instance, a disease named little leaf disease is a hazardous disease found (...) in pine trees in United States. Detection of plant disease through some automatic technique is beneficial as it reduces a large work of monitoring in big farms of crops, and at very early stage itself if detected properly by identifying the symptoms of diseases can result in increased productivity. This paper presents an algorithm for image segmentation technique which is used for automatic detection and classification of plant leaf diseases. It also covers diseases classification techniques that can be used for plant leaf disease detection. Image segmentation is one of the method which will segment the raw images in to two or more clusters and the programmed algorithm will work fine in analyzing these clusters for disease classification and prediction of type of disease that a plant leaf gets affected. (shrink)

Jonathan Y. Tsou examines and defends positions on central issues in philosophy of psychiatry. The positions defended assume a naturalistic and realist perspective and are framed against skeptical perspectives on biological psychiatry. Issues addressed include the reality of mental disorders; mechanistic and disease explanations of abnormal behavior; definitions of mental disorder; natural and artificial kinds in psychiatry; biological essentialism and the projectability of psychiatric categories; looping effects and the stability of mental disorders; psychiatric classification; and the validity of (...) the DSM's diagnostic categories. The main argument defended by Tsou is that genuine mental disorders are biological kinds with harmful effects. This argument opposes the dogma that mental disorders are necessarily diseases that result from biological dysfunction. Tsou contends that the broader ideal of biological kinds offers a more promising and empirically ascertainable naturalistic standard for assessing the reality of mental disorders and the validity of psychiatric categories. (shrink)

In his target article and recent interesting book about addiction and the brain, Marc Lewis claims that the prevalent medical view of addiction as a brain disease or a disorder, is mistaken. In this commentary we critically examine his arguments for this claim. We find these arguments to rest on some problematical and largely undefended assumptions about notions of disease, disorder and the demarcation between them and good health. Even if addiction does seem to differ from some typical (...) brain diseases, we believe contrary to Lewis, that there are still good reasons to maintain its classification as a mental or behavioral disorder. (shrink)

In this paper, I offer one example of conceptual change. Specifically, I contend that the discovery that viruses could cause cancer represents an excellent example of branch jumping, one of Thagard’s nine forms of conceptual change. Prior to about 1960, cancer was generally regarded as a degenerative, chronic, non-infectious disease. Cancer causation was therefore usually held to be a gradual process of accumulating cellular damage, caused by relatively non-specific component causes, acting over long periods of time. Viral infections, on (...) the other hand, were generally understood to be acute processes, whereby single, specific and necessary causal agents acted alone to produce disease. However, during the 1960s and 1970s, a number of cancers were discovered to have an infectious aetiology. Of particular note were two—Burkitt’s lymphoma and cervical cancer—which I will discuss in detail later in this piece. Together, these discoveries led, in the short term, to a tentative aetiological reclassification of some types of cancer as infectious diseases and, in the longer term, to a full-blown reclassification of cancer as an aetiological disease branch in its own right. This process of reclassification forms the empirical basis for my concluding remarks on the influence of classification upon causation in medicine. Through this, I aim to demonstrate that conceptual change, far from being a purely abstract concern of the philosopher of science, is of substantial import to scientific practitioners. (shrink)

In computer science, Artificial Intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals. Computer science defines AI research as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals. Deep Learning is a new field of research. One of the branches of Artificial Intelligence Science deals with the creation of theories and algorithms that (...) allow the machine to learn by simulating neurons in the human body. Most in-depth learning research focuses on finding high-level methods. The strippers analyze a large data set using linear and nonlinear transformations. The method of deep learning is used in the detection of several diseases including blood cell diseases and their classification using the radiography of blood cells to help decision makers to know the type of blood cell and its associated diseases and the results will be presented in detail and discussed. This thesis is using python language and deep learning to detect blood cell diseases and their classifications. The proposed deep learning model was trained, validated and the tested. The accuracy of proposed model was 98.00%. (shrink)

In this paper an Artificial Neural Network (ANN) model, for predicting the category of a tumor was developed and tested. Taking patients’ tests, a number of information gained that influence the classification of the tumor. Such information as age, sex, histologic-type, degree-of-diffe, status of bone, bone-marrow, lung, pleura, peritoneum, liver, brain, skin, neck, supraclavicular, axillar, mediastinum, and abdominal. They were used as input variables for the ANN model. A model based on the Multilayer Perceptron Topology was established and (...) trained using data set which its title is “primary tumor” and was obtained from the University Medical Centre, Institute of Oncology, Ljubljana, Yugoslavia Test data evaluation shows that the ANN model is able to correctly predict the tumor category with 76.67 % accuracy. (shrink)

By leveraging convolutional neural networks (CNNs), the model identifies various plant diseases with high accuracy. The experimental setup includes a dataset consisting of healthy and diseased leaf images of different plant species. The dataset is preprocessed to remove noise and augmented to address the issue of class imbalance. The CNN model is then trained, validated, and tested on this dataset. The results indicate that the deep learning model achieves a classification accuracy of over 95% for most plant diseases. Additionally, (...) the system is designed to provide real-time feedback to farmers, helping them take immediate corrective action. This automated approach eliminates the need for expert human intervention and can be deployed on mobile devices for ease of use in rural areas. The potential future applications of this system include integration with precision agriculture techniques and expansion to a wider range of crops. (shrink)

The three target articles constitute a powerful and persuasive call for actively anti-racist bioethics and biomedicine. All three articles reject race as a biological category. Nevertheless, they share a common commitment to racial classification. At one point, Ruqaiijah Yearby writes that “social race, like biological race, is an illusion created to establish racial hierarchy,” but mostly she writes about “races” as though they were not an illusion, but a reality. In this commentary I critique the racial realism of the (...) target articles. I argue that the biomedical fight against racism is best served by a form of anti-realist reconstructionism about race, which says that there are no races, only racialized groups—groups misunderstood to be races. Note that I do not defend the color blindness that Clarence H. Braddock (2020) persuasively rejects. Because of the role of skin color in racialization and racism, color blindness is a misguided goal. One can reject color blindness, though, without accepting racial realism... (shrink)

This paper focuses on invasive therapeutic procedures, defined as procedures requiring the introduction of hands, instruments, or devices into the body via incisions or punctures of the skin or mucous membranes performed with the intent of changing the natural history of a human disease or condition for the better. Ethical and methodological concerns have been expressed about studies designed to evaluate the effects of invasive therapeutic procedures. Can such studies meet the same standards demanded of those, for example, (...) evaluating pharmaceutical agents? This paper describes a research project aimed at examining the interplay and sometimes apparent conflict between ethical standards for human research and standards for methodological rigor in trials of invasive procedures. The paper discusses how the authors plan to develop a set of consensus standards that, if met, would result in substantial and much-needed improvements in the methodological and ethical quality of such trials. (shrink)

Clinical microbiology serves as a partner to clinicians in the diagnosis and treatment of infectious diseases. Antibiotics are prescribed empirically before the availability of antimicrobial susceptibility testing data, especially when the patient's medical status could deteriorate by suspending the treatment. To investigate the impact of antimicrobial susceptibility testing on the management of outpatients with suspected bacterial infection in Libyan patients, a cross-sectional prospective study concluded on microbial microdroplet culture by including outpatients with suspected bacterial infection, who have done antimicrobial susceptibility (...) testing. Thus, 53 Libyan patients with urinary tract infections and 14 Libyan patients with skin infections were included in this study. Before the availability of antimicrobial susceptibility testing data, 25 patients were appropriately treated while 42 patients were inappropriately treated. After the availability of antimicrobial susceptibility testing data, the number of appropriately treated patients increased to 57 patients. Thus, antimicrobial susceptibility testing improved the management of 41 patients by discontinuing an unnecessary antibiotic in four patients, starting necessary antibiotic therapy in 18 patients, and changing to more appropriate antibiotic in 19 patients. However, the antimicrobial susceptibility testing has no impact on the management of 24 patients and has led to the worsening of the management of two patients. The effect of antimicrobial susceptibility testing of antibiotic de-escalation was assessed in 35 patients; however, antibiotic de-escalation occurred in six patients. Thus, data of antimicrobial susceptibility testing has improved the management of Libyan outpatients with bacterial infection but their role in antibiotic de-escalation was slight. (shrink)

The 2013 Rostock Symposium on Systems Biology and Bioinformatics in Aging Research was again dedicated to dissecting the aging process using in silico means. A particular focus was on ontologies, as these are a key technology to systematically integrate heterogeneous information about the aging process. Related topics were databases and data integration. Other talks tackled modeling issues and applications, the latter including talks focussed on marker development and cellular stress as well as on diseases, in particular on diseases of kidney (...) and skin. (shrink)

Our philosophical understanding of mental illness is being shaped by neuroscience. However, it has the paradoxical effect of igniting two radically opposed groups of philosophical views. On one side, skepticism and denialism assume that, lacking clear biological mechanisms and etiologies for most mental illnesses, we should infer they are constructions best explained by means of social factors. This is strongly associated with medical nihilism: it considers psychiatry more harmful than benign. On the other side of the divide, naturalism and reductionism (...) are on the look for failures in the biological functioning of the organism whenever a genuine mental illness occurs. Psychiatry as currently practiced, accordingly, exhibits the gaps of an ongoing research programme; a yet to be completed neuroscience would link mental illnesses with identifiable biological mechanisms. Both sides of this divide claim to be fostered by scientific discoveries and advances in neuroscience, when taken at face value. Against this background, we argue instead for a modest view. To that end, we draw attention to some nuances in the scientific realism debate. While contending that neuroscientific theories and models aim to provide true representations of their target systems, and can justifiably claim to have attained some, we argue that our confidence should not be placed beforehand in specific features of these scientific representations. Hence, it would be unwarranted to extract morals for psychiatry from posits (or their absence) in neuroscientific explanations of mental illnesses. To illustrate our position, we examine some recent discoveries in neuroscience concerning bipolar disorder. We conclude by linking our topic to a broader issue in the philosophy of medicine: insofar as psychiatry is a biomedical specialty, its classifications of health and disease are guided by pragmatic concerns, as well as by scientific discoveries. (shrink)

Biomedical ontologies are emerging as critical tools in genomic and proteomic research where complex data in disparate resources need to be integrated. A number of ontologies exist that describe the properties that can be attributed to proteins; for example, protein functions are described by Gene Ontology, while human diseases are described by Disease Ontology. There is, however, a gap in the current set of ontologies—one that describes the protein entities themselves and their relationships. We have designed a PRotein Ontology (...) (PRO) to facilitate protein annotation and to guide new experiments. The components of PRO extend from the classification of proteins on the basis of evolutionary relationships to the representation of the multiple protein forms of a gene (products generated by genetic variation, alternative splicing, proteolytic cleavage, and other post-translational modification). PRO will allow the specification of relationships between PRO, GO and other OBO Foundry ontologies. Here we describe the initial development of PRO, illustrated using human proteins from the TGF-beta signaling pathway. (shrink)

Are psychiatric disorders natural kinds? This question has received a lot of attention within present-day philosophy of psychiatry, where many authors debate the ontology and nature of mental disorders. Similarly, historians of psychiatry, dating back to Foucault, have debated whether psychiatric researchers conceived of mental disorders as natural kinds or not. However, historians of psychiatry have paid little to no attention to the influence of (a) theories within logic, and (b) theories within metaphysics on psychiatric accounts of proper method, and (...) on accounts of the nature and classification of mental disorders. Historically, however, logic and metaphysics have extensively shaped methods and interpretations of classifications in the natural sciences. This paper corrects this lacuna in the history of psychiatry, and demonstrates that theories within logic and metaphysics, articulated by Christian Wolff (1679-1754), have significantly shaped the conception of medical method and (psychiatric) nosology of the influential nosologist Boissier De Sauvages (1706-1767). After treating Sauvages, I discuss the method of the influential nosologist William Cullen (1710-1790), and demonstrate the continuity between the classificatory methods of Sauvages and Cullen. I show that both Sauvages and Cullen were essentialists concerning medical diseases in general and psychiatric disorders in particular, contributing to the history of conceptions of the ontology and nature of mental disorders. (shrink)

There is a lot of conceptual engineering going on in medical research. I substantiate this claim with two examples, the medical debate about cancer classification and about obesity as a disease I also argue that the proper target of conceptual engineering in medical research are experts’ conceptions. These are explicitly written down in documents and guidelines, and they bear on research and policies. In the second part of the chapter, I propose an externalist framework in which conceptions have (...) both the explanatory power of psychological concepts and that of semantic concepts. It is likely, however, that human activities and practices distinct from medical research, and regulated by different practices and epistemic rules, call for different targets for conceptual engineering. I conclude with indicating an open agenda of problems for philosophers of medicine interested in conceptual engineering. (shrink)

This paper asks when a natural disease kind is truly 'reactive' and when it is merely associated with a corresponding social kind. I begin with a permissive account of real kinds and their structure, distinguishing natural kinds, indifferent kinds and reactive kinds as varieties of real kind characterised by super-explanatory properties. I then situate disease kinds within this framework, arguing that many disease kinds prima facie are both natural and reactive. I proceed to distinguish ‘simple dependence’, ‘secondary (...) dependence’ and ‘essential dependence’ between a natural kind and its classification, and argue that a natural kind is only really reactive, in an important sense, under conditions of essential dependence. On this basis, I offer a principled hypothesis for why psychiatric kinds tend to be more metaphysically unstable than paradigm somatic disease kinds. (shrink)

A pressing need for interrater reliability in the diagnosis of mental disorders emerged during the mid-twentieth century, prompted in part by the development of diverse new treatments. The Diagnostic and Statistical Manual of Mental Disorders (DSM), third edition answered this need by introducing operationalized diagnostic criteria that were field-tested for interrater reliability. Unfortunately, the focus on reliability came at a time when the scientific understanding of mental disorders was embryonic and could not yield valid disease definitions. Based on accreting (...) problems with the current DSM-fourth edition (DSM-IV) classification, it is apparent that validity will not be achieved simply by refining criteria for existing disorders or by the addition of new disorders. Yet DSM-IV diagnostic criteria dominate thinking about mental disorders in clinical practice, research, treatment development, and law. As a result, the modernDSMsystem, intended to create a shared language, also creates epistemic blinders that impede progress toward valid diagnoses. Insights that are beginning to emerge from psychology, neuroscience, and genetics suggest possible strategies for moving forward. (shrink)

The guiding premise from which this special report begins is the conviction and hope that justice is at the normative heart of medicine and that it is the perpetual task of bioethics to bring concerns of justice to bear on medical practice. On such an account, justice is medicine's lifeblood, that by which it contributes to life as opposed to diminishing it. It is in this larger, historical, intersectional, critical, and ethically minded context that we must approach pressing questions facing (...) medicine, including the question of the import and role of genomic knowledge for human life. The second premise is that, at least in principle, the knowledge generated by genomics can be a gift or a weight, or both at the same time. That is to say that, on the one hand, genomic knowledge is a gift, creating novel insights into the genetic drivers of disease and into the geographical paths of our ancestors. And on the other hand, it is a weight, creating new obligations, new forms of social classification, and new forms of surveillance. Because it is in many ways the “common sense” of the day that genomic knowledge is a gift, this special report, which contains nine essays, concentrates on the ways in which such knowledge can be a weight, a weight that has the potential to thwart—and historically has thwarted—medicine from genuinely advancing justice . (shrink)

In view of the publication of the DSM-V researchers were asked to discuss the theoretical implications of the definition of mental disorders. The reasons for the use, in the DSM-III, of the term disorder instead of disease are considered. The analysis of these reasons clarifies the distinction between the general definition of disorder and its implicit, technical meaning which arises from concrete use in DSM disorders. The characteristics and limits of this technical meaning are discussed and contrasted to alternative (...) definitions, like Wakefield’s harmful/dysfunction analysis. It is shown that Wakefield’s analysis faces internal theoretical problems in addition to practical limits for its acceptance in the DSM-V. In particular, it is shown that: a) the term dysfunction is not purely factual but intrinsically normative/evaluative; b) it is difficult to clarify what dysfunctions are in the psychiatric context (the dysfunctional mechanism involved being unknown in most cases and the use of evolutionary theory being even more problematic); c) the use of conceptual analysis and commonsense intuition to define dysfunctions leaves unsatisfied empiricists; d) it is unlikely that the authors of the DSM-V will accept Wakefield’s suggestion to revise the diagnostic criteria of any single DSM disorder in accordance with his analysis, because this is an excessively extensive change and also because this would probably reduce DSM reliability. In conclusion, it is pointed up in which sense DSM mental disorders have to be conceived as constructs, and that this undermines the realistic search for a clear-cut demarcation criterion between what is disorder and what is not. (shrink)