Results for 'Medical Imaging'

977 found
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  1. Advancements in AI for Medical Imaging: Transforming Diagnosis and Treatment.Zakaria K. D. Alkayyali, Ashraf M. H. Taha, Qasem M. M. Zarandah, Bassem S. Abunasser, Alaa M. Barhoom & Samy S. Abu-Naser - 2024 - International Journal of Academic Engineering Research(Ijaer) 8 (8):8-15.
    Abstract: The integration of Artificial Intelligence (AI) into medical imaging represents a transformative shift in healthcare, offering significant improvements in diagnostic accuracy, efficiency, and patient outcomes. This paper explores the application of AI technologies in the analysis of medical images, focusing on techniques such as convolutional neural networks (CNNs) and deep learning models. We discuss how these technologies are applied to various imaging modalities, including X-rays, MRIs, and CT scans, to enhance disease detection, image segmentation, and (...)
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  2. Medical Image Classification with Machine Learning Classifier.Destiny Agboro - forthcoming - Journal of Computer Science.
    In contemporary healthcare, medical image categorization is essential for illness prediction, diagnosis, and therapy planning. The emergence of digital imaging technology has led to a significant increase in research into the use of machine learning (ML) techniques for the categorization of images in medical data. We provide a thorough summary of recent developments in this area in this review, using knowledge from the most recent research and cutting-edge methods.We begin by discussing the unique challenges and opportunities associated (...)
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  3. Harnessing Artificial Intelligence to Enhance Medical Image Analysis.Malak S. Hamad, Mohammed H. Aldeeb, Mohammed M. Almzainy, Shahd J. Albadrasawi, Musleh M. Musleh, Bassem S. Abu-Nasser & Samy S. Abu-Naser - 2024 - International Journal of Academic Health and Medical Research (IJAHMR) 8 (9):1-7.
    Abstract: The integration of Artificial Intelligence (AI) into medical imaging marks a transformative advancement in healthcare, significantly enhancing diagnostic accuracy, efficiency, and patient outcomes. This paper delves into the application of AI technologies in medical image analysis, with a particular focus on techniques such as convolutional neural networks (CNNs) and deep learning models. We examine how these technologies are employed across various imaging modalities, including X-rays, MRIs, and CT scans, to improve disease detection, image segmentation, and (...)
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  4.  63
    Real-Time Medical Image Analysis and Guidance Via Telegram Chatbot: Bridging Accessibility Gaps in Urban Healthcare.P. Shivathmika - 2025 - International Journal of Engineering Innovations and Management Strategies 1 (10):1-12.
    Additionally, privacy and security concerns surrounding medical data are addressed by ensuring compliance with stringent data protection standards such as HIPAA and GDPR, thereby ensuring users that their medical information is handled securely and confidentially. The solution aims to improve healthcare accessibility, reduce delays in diagnosis, and provide users with greater control over their health management. This report outlines the chatbot’s design, the integration of image processing techniques, the use of machine learning models for disease identification, and the (...)
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  5. Leveraging Machine Learning Algorithms for Medical Image Classification Introduction.Ugochukwu Llodinso - manuscript
    The use of machine learning to medical image classification has seen significant development and implementation in the last several years. Computers can learn to identify patterns, make predictions, and use data to inform their judgements; this capability is known as machine learning, a branch of Artificial intelligence (AI). Classifying images according to their contents allows us to do things like identify the type of sickness, organ, or tissue depicted. Medical picture classification and interpretation using machine learning algorithms has (...)
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  6. The Use of Machine Learning Methods for Image Classification in Medical Data.Destiny Agboro - forthcoming - International Journal of Ethics.
    Integrating medical imaging with computing technologies, such as Artificial Intelligence (AI) and its subsets: Machine learning (ML) and Deep Learning (DL) has advanced into an essential facet of present-day medicine, signaling a pivotal role in diagnostic decision-making and treatment plans (Huang et al., 2023). The significance of medical imaging is escalated by its sustained growth within the realm of modern healthcare (Varoquaux and Cheplygina, 2022). Nevertheless, the ever-increasing volume of medical images compared to the availability (...)
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  7. A Novel Method for Detecting Liver Tumors combining Machine Learning with Medical Imaging in CT Scans using ResUNet.M. Prakash P. Manjula, K. Krishnakumar, Smitha Gl, S. Pandiaraj - 2024 - International Conference on Integrated Circuits and Communication Systems 1 (1):1-5.
    The utilization of machine learning optimization methods is of utmost importance in the identification of liver tumors, attracting considerable interest in this domain. After obtaining a liver tissue sample, magnetic resonance imaging (MRI), computed tomography (CT), and ultrasonography (US) are used as imaging techniques to separate the tumor and liver. Nevertheless, the utilization of shades of gray and forms is insufficient for achieving accurate segmentation in computed abdominal CT images, mostly because of the presence of overlapping intensities and (...)
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  8. Reliability of molecular imaging diagnostics.Elisabetta Lalumera, Stefano Fanti & Giovanni Boniolo - 2021 - Synthese (S23):5701-5717.
    Advanced medical imaging, such as CT, fMRI and PET, has undergone enormous progress in recent years, both in accuracy and utilization. Such techniques often bring with them an illusion of immediacy, the idea that the body and its diseases can be directly inspected. In this paper we target this illusion and address the issue of the reliability of advanced imaging tests as knowledge procedures, taking positron emission tomography in oncology as paradigmatic case study. After individuating a suitable (...)
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  9. From Photography to fMRI: Epistemic Functions of Images in Medical Research on Hysteria.Paula Muhr - 2022 - Bielefeld: Transcript.
    Hysteria, a mysterious disease known since antiquity, is said to have ceased to exist. Challenging this commonly held view, this is the first cross-disciplinary study to examine the current functional neuroimaging research into hysteria and compare it to the nineteenth-century image-based research into the same disorder. Paula Muhr's central argument is that, both in the nineteenth-century and the current neurobiological research on hysteria, images have enabled researchers to generate new medical insights. Through detailed case studies, Muhr traces how different (...)
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  10. Developing the Quantitative Histopathology Image Ontology : A case study using the hot spot detection problem.Metin Gurcan, Tomaszewski N., Overton John, A. James, Scott Doyle, Alan Ruttenberg & Barry Smith - 2017 - Journal of Biomedical Informatics 66:129-135.
    Interoperability across data sets is a key challenge for quantitative histopathological imaging. There is a need for an ontology that can support effective merging of pathological image data with associated clinical and demographic data. To foster organized, cross-disciplinary, information-driven collaborations in the pathological imaging field, we propose to develop an ontology to represent imaging data and methods used in pathological imaging and analysis, and call it Quantitative Histopathological Imaging Ontology – QHIO. We apply QHIO to (...)
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  11. Randomized Controlled Trials for Diagnostic Imaging: Conceptual and Pratical Problems.Elisabetta Lalumera & Stefano Fanti - 2019 - Topoi 38 (2):395-400.
    We raise a problem of applicability of RCTs to validate nuclear diagnostic imaging tests. In spite of the wide application of PET and other similar techniques that use radiopharmaceuticals for diagnostic purposes, RCT-based evidence on their validity is sparse. We claim that this is due to a general conceptual problem that we call Prevalence of Treatment, which arises in connection with designing RCTs for testing any diagnostic procedure in the present context of medical research, and is particularly apparent (...)
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  12. Medicinal Plants Identification through Image processing and Machine Learning.G. Kiran Kumar - 2025 - International Journal of Engineering Innovations and Management Strategies 1 (1):1-11.
    The project is aimed at an arduous task of precise identification of medicinal plant species with the problem being pertinent in those industries that include botany, Ayurveda, pharmacology, and biomedical research. Most of the traditional identification methods are quite serious challenges for users, researchers, and students because they are usually time-consuming, knowledge-intensive, and prone to human errors. Our proposal develops an advanced web-based application for this process by utilizing state-of-the-art methods in image processing and machine learning. We will create a (...)
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  13. Limits of trust in medical AI.Joshua James Hatherley - 2020 - Journal of Medical Ethics 46 (7):478-481.
    Artificial intelligence (AI) is expected to revolutionise the practice of medicine. Recent advancements in the field of deep learning have demonstrated success in variety of clinical tasks: detecting diabetic retinopathy from images, predicting hospital readmissions, aiding in the discovery of new drugs, etc. AI’s progress in medicine, however, has led to concerns regarding the potential effects of this technology on relationships of trust in clinical practice. In this paper, I will argue that there is merit to these concerns, since AI (...)
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  14. Advancements in AI-Enhanced OCT Imaging for Early Disease Detection and Prevention in Aging Populations.Nushra Tul Zannat Sabira Arefin, Marcia A. Orozco, Ms Bme - 2025 - International Journal of Multidisciplinary Research in Science, Engineering and Technology 8 (3):1430-1444.
    Optical Coherence Tomography (OCT) proves essential as an imaging modality for detecting early diseases especially by helping patients who age and face increased susceptibility to retinal and systemic conditions. The development of artificial intelligence technology now boosts OCT diagnostic features to identify conditions like diabetic retinopathy in addition to age-related macular degeneration and cardiovascular diseases at an early stage. This paper examines two main advancements in artificial intelligence for OCT imaging monitoring such as Google Health's Retinal Disease Predictor (...)
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  15. Speech acts and medical records: The ontological nexus.Lowell Vizenor & Barry Smith - 2004 - In Jana Zvárová, Proceedings of the International Joint Meeting EuroMISE 2004.
    Despite the recent advances in information and communication technology that have increased our ability to store and circulate information, the task of ensuring that the right sorts of information gets to the right sorts of people remains. We argue that the many efforts underway to develop efficient means for sharing information across healthcare systems and organizations would benefit from a careful analysis of human action in healthcare organizations. This in turn requires that the management of information and knowledge within healthcare (...)
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  16. Organization of the corporate style of the medical institution: functions and components.Oleksandr P. Krupskyi & Yuliya Stasiuk - 2023 - Time Description of Economic Reforms 1:87-95.
    Today's realities require medical institutions to take more careful account of intangible factors that make up an irreplaceable component of cultural characteristics. Changes in the socio-economic conditions of economic activity have led to increased attention of the management of medical institutions to the need to form a corporate style that will provide additional competitive advantages. The purpose of the study is to identify the functions and elements of the corporate style of a medical institution and its subdivisions, (...)
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  17. Hybrid Blockchain and Big Data Framework for PrivacyPreserving Medical Data Sharing.P. Selvaprasanth - 2024 - Journal of Theoretical and Computationsl Advances in Scientific Research (Jtcasr) 8 (1):1-7.
    In the healthcare sector, the need for privacy-preserving and secure data sharing is paramount, especially as the volume of medical data continues to grow due to advancements in big data and digital health technologies. To address these challenges, a hybrid blockchain and big data framework offers a promising solution for secure medical data sharing. Blockchain technology provides a decentralized and immutable ledger that ensures the security, transparency, and privacy of medical data. However, traditional blockchain systems face scalability (...)
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  18. Medicine, symbolization and the 'real' body: Lacan's understanding of medical science.Hub Zwart - 1998 - Medicine, Health Care and Philosophy 1 (2):107-117.
    Throughout the 20th century, philosophers have criticized the scientific understanding of the human body. Instead of presenting the body as a meaningful unity or Gestalt, it is regarded as a complex mechanism and described in quasi-mechanistic terms. In a phenomenological approach, a more intimate experience of the body is presented. This approach, however, is questioned by Jacques Lacan. According to Lacan, three basic possibilities of experiencing the body are to be distinguished: the symbolical (or scientific) body, the imaginary (or ideal) (...)
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  19. Ethical considerations in functional magnetic resonance imaging research in acutely comatose patients.Charles Weijer, Tommaso Bruni, Teneille Gofton, G. Bryan Young, Loretta Norton, Andrew Peterson & Adrian M. Owen - 2015 - Brain:0-0.
    After severe brain injury, one of the key challenges for medical doctors is to determine the patient’s prognosis. Who will do well? Who will not do well? Physicians need to know this, and families need to do this too, to address choices regarding the continuation of life supporting therapies. However, current prognostication methods are insufficient to provide a reliable prognosis. -/- Functional Magnetic Resonance Imaging (MRI) holds considerable promise for improving the accuracy of prognosis in acute brain injury (...)
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  20. Evaluation of the Image Quality of Ultra-Low-Dose Paranasal Sinus Computed Tomography Scans.Melih Akşamoğlu & Mehmet Sait Menzilcioğlu - 2023 - European Journal of Therapeutics 29 (2):143-148.
    Objective: We aimed to investigate the image quality of paranasal sinus computed tomography (CT) scans obtained with the “Advanced intelligent Clear-IQ Engine” (AICE) software and ultra-low dose parameters in patients with prediagnosed rhinitis, sinusitis or nasal septum deviation. -/- Methods: The first 50 patients (31 men and 19 women, aged 18-70 years) who agreed to participate in our prospectively planned study were included in the study. Imaging of the patients was performed with a 160-slice multidetector CT device Canon Aquilion (...)
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  21.  57
    AUTOMATED PNEUMONIA DETECTION USING DEEP LEARNING AND CHEST X-RAY IMAGES.K. Mahesh - 2024 - International Journal of Engineering Innovations and Management Strategies, 1 (5):1-14.
    Pneumonia is a serious respiratory infection that poses significant health risks, particularly if not diagnosed and treated promptly. Traditional methods of pneumonia diagnosis rely on the manual interpretation of chest X-ray images by radiologists, a process that can be time-consuming, subjective, and error-prone, especially in regions with limited access to experienced medical professionals. To address these challenges, this study explores the development of an automated deep learning-based system for pneumonia detection using chest X-ray images. The results demonstrate that the (...)
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  22.  48
    Breast Cancer Detection Using Machine Learning.Shifa A. M. Amrutha D. - 2024 - International Journal of Innovative Research in Science, Engineering and Technology 13 (11):19401-19406.
    Breast cancer is one of the leading causes of death among women worldwide. Early detection plays a crucial role in improving survival rates, and machine learning (ML) provides powerful tools for identifying cancerous tumors in medical imaging and diagnostic data. This paper explores various machine learning techniques used for breast cancer detection, with a particular focus on the Wisconsin Breast Cancer Dataset (WBCD). We evaluate several classification models, including Logistic Regression (LR), Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), (...)
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  23. Classification of Anomalies in Gastrointestinal Tract Using Deep Learning.Ibtesam M. Dheir & Samy S. Abu-Naser - 2022 - International Journal of Academic Engineering Research (IJAER) 6 (3):15-28.
    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 (...)
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  24. An Experimental Analysis of Revolutionizing Banking and Healthcare with Generative AI.Sankara Reddy Thamma - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):580-590.
    Generative AI is reshaping sectors like banking and healthcare by enabling innovative applications such as personalized service offerings, predictive analytics, and automated content generation. In banking, generative AI drives customer engagement through tailored financial advice, fraud detection, and streamlined customer service. Meanwhile, in healthcare, it enhances medical imaging analysis, drug discovery, and patient diagnostics, significantly impacting patient care and operational efficiency. This paper presents an experimental study examining the implementation and effectiveness of generative AI in these sectors.
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  25. Classification of Alzheimer’s Disease Using Traditional Classifiers with Pre-Trained CNN.Husam R. Almadhoun & Samy S. Abu-Naser - 2021 - International Journal of Academic Health and Medical Research (IJAHMR) 5 (4):17-21.
    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 (...) 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)
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  26. Advanced Data Integration for Smart Healthcare: Leveraging Blockchain and AI Technologies.A. Manoj Prabaharan - 2024 - Journal of Artificial Intelligence and Cyber Security (Jaics) 8 (1):1-7.
    The healthcare sector is undergoing a transformative shift towards smart healthcare, driven by advancements in technology, including Artificial Intelligence (AI) and Blockchain. As healthcare systems generate vast amounts of data from multiple sources, such as electronic health records (EHRs), medical imaging, wearable devices, and sensor-based monitoring, the challenge lies in securely integrating and analyzing this data for real-time, actionable insights. Blockchain technology, with its decentralized, immutable, and transparent framework, offers a robust solution for securing data integrity, privacy, and (...)
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  27.  99
    DISTORTED FACE RECONSTRUCTION USING 3D CNN.S. Yoheswari - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):567-574.
    This comparison helps in identifying which model performs better in the task of facial reconstruction from distorted images. Visualizing the results in the form of a graph provides a clear and concise way to understand the comparative performance of the algorithms. The ultimate goal of this project is to develop a system that can accurately reconstruct distorted faces, which can be invaluable in identifying accident victims or assisting in medical treatments. By providing a reliable method for facial reconstruction, this (...)
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  28. Multimodal Artificial Intelligence in Medicine.Joshua August Skorburg - forthcoming - Kidney360.
    Traditional medical Artificial Intelligence models, approved for clinical use, restrict themselves to single-modal data e.g. images only, limiting their applicability in the complex, multimodal environment of medical diagnosis and treatment. Multimodal Transformer Models in healthcare can effectively process and interpret diverse data forms such as text, images, and structured data. They have demonstrated impressive performance on standard benchmarks like USLME question banks and continue to improve with scale. However, the adoption of these advanced AI models is not without (...)
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  29. The quantization error in a Self-Organizing Map as a contrast and color specific indicator of single-pixel change in large random patterns.Birgitta Dresp-Langley - 2019 - Neural Networks 120:116-128..
    The quantization error in a fixed-size Self-Organizing Map (SOM) with unsupervised winner-take-all learning has previously been used successfully to detect, in minimal computation time, highly meaningful changes across images in medical time series and in time series of satellite images. Here, the functional properties of the quantization error in SOM are explored further to show that the metric is capable of reliably discriminating between the finest differences in local contrast intensities and contrast signs. While this capability of the QE (...)
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  30.  59
    Facial Distortion Reconstruction with 3D Convolutional Neural Networks.M. Sheik Dawood - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):575-590.
    . The accuracy levels of VGG19 and 3D CNN are compared using the performance metrics. This comparison helps in identifying which model performs better in the task of facial reconstruction from distorted images. Visualizing the results in the form of a graph provides a clear and concise way to understand the comparative performance of the algorithms. The ultimate goal of this project is to develop a system that can accurately reconstruct distorted faces, which can be invaluable in identifying accident victims (...)
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  31. Psychological Counseling for Adult Clients.Igor Bushai - 2018 - Psychology and Psychosocial Interventions 1:20-24.
    The article analyzes methodological and practical approaches of psychological counseling for adult clients. General psychological problems of this age group are generalized; the concept of restoration of balance between the image of the world and the image of the “I” of clients as the main internal mechanism of mental equilibrium is substantiated; the aspects of professional training of a psychologist to advisory practice with adults are considered. -/- The urgent problem of psychological counseling for adult clients is to help them (...)
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  32. Philosophical Ruminations about Embryo Experimentation with Reference to Reproductive Technologies in Jewish “Halakhah”.Piyali Mitra - 2017 - IAFOR Journal of Ethics, Religion and Philosophy 3 (2):5-19.
    The use of modern medical technologies and interventions involves ethical and legal dilemmas which are yet to be solved. For the religious Jews the answer lies in Halakhah. The objective of this paper is to unscramble the difficult conundrum possessed by the halakhalic standing concerning the use of human embryonic cell for research. It also aims to take contemporary ethical issues arising from the use of technologies and medical advances made in human reproduction and study them from an (...)
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  33. Diagnosing Breast Cancer Using Expert System.Suheir H. Almurshidi - 2018 - Dissertation, Al-Azhar University, Gaza
    The “Expert System for Diagnosing Breast Cancer" is used to assist medical students to improve their education on diagnosis and counseling the process of analyzing the biopsy image of the microscope, determining the type of tumor and the treatment method for each case and identifying the disease related questions. According to the Ministry of Health in its annual report in Gaza, between 2009 and 2014 there are 7069 cases of breast cancer, and in 2014 there are 1502 cases of (...)
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  34. Breast Cancer Knowledge Based System.Mohammed H. Aldeeb & Samy S. Abu-Naser - 2023 - International Journal of Engineering and Information Systems 7 (6):46-51.
    Abstract: The Knowledge-Based System for Diagnosing Breast Cancer aims to support medical students in enhancing their education regarding diagnosis and counseling. The system facilitates the analysis of biopsy images under a microscope, determination of tumor type, selection of appropriate treatment methods, and identification of disease-related questions. According to the Ministry of Health's annual report in Gaza, there were 7,069 cases of breast cancer between 2009 and 2014, with 1,502 cases reported in 2014. In an era dominated by visual information, (...)
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  35. “Empiricism contra Experiment: Harvey, Locke and the Revisionist View of Experimental Philosophy”.Alan Salter & Charles T. Wolfe - 2009 - Bulletin d'histoire et d'épistémologie des sciences de la vie 16 (2):113-140.
    In this paper we suggest a revisionist perspective on two significant figures in early modern life science and philosophy: William Harvey and John Locke. Harvey, the discoverer of the circulation of the blood, is often named as one of the rare representatives of the ‘life sciences’ who was a major figure in the Scientific Revolution. While this status itself is problematic, we would like to call attention to a different kind of problem: Harvey dislikes abstraction and controlled experiments (aside from (...)
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  36. Seeing, Feeling, Doing: Mandatory Ultrasound Laws, Empathy and Abortion.Catherine Mills - 2018 - Journal of Practical Ethics 6 (2):1-31.
    In recent years, a number of US states have adopted laws that require pregnant women to have an ultrasound examination, and be shown images of their foetus, prior to undergoing a pregnancy termination. In this paper, I examine one of the basic presumptions of these laws: that seeing one’s foetus changes the ways in which one might act in regard to it, particularly in terms of the decision to terminate the pregnancy or not. I argue that mandatory ultrasound laws compel (...)
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  37.  23
    Using Machine Learning tools to Calculate Multi Slice Multi Echo (MSME) Score for Alzheimer's Diagnosis.Yalamati Sreedhar - 2024 - International Journal of Innovations in Scientific Engineering 19 (1):49-67.
    Alzheimer's disease (AD) poses a significant public health challenge. The hippocampus is one of the most affected brain regions and a readily accessible biomarker for diagnosis through MRI imaging in machine learning applications. However, utilizing entire MRI image slices in machine learning for AD classification has shown reduced accuracy. This study introduces the novel 'select slices' method, which involves identifying and focusing on specific landmarks within the hippocampus region in MRI images. This approach aims to improve classification accuracy by (...)
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  38. Knowledge-Based System for the Diagnosis of Flatulence.Jihad Tantawi & Samy S. Abu-Naser - 2023 - International Journal of Academic Engineering Research (IJAER) 7 (6):23-29.
    Diagnosing flatulence involves a thorough assessment of an individual's symptoms, medical history, and, if necessary, the use of diagnostic tests. Healthcare providers gather information about the patient's medical background and conduct a physical examination to identify any signs of gastrointestinal issues. Dietary habits are evaluated, and potential triggers are identified through an elimination diet. Diagnostic tests such as breath tests, stool analysis, or imaging studies may be performed to further investigate the underlying causes of excessive flatulence. Accurate (...)
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  39. Nerve/Nurses of the Cosmic Doctor: Wang Yang-ming on Self-Awareness as World-Awareness.Joshua M. Hall - 2016 - Asian Philosophy 26 (2):149-165.
    In Philip J. Ivanhoe’s introduction to his Readings from the Lu-Wang School of Neo-Confucianism, he argues convincingly that the Ming-era Neo-Confucian philosopher Wang Yang-ming (1472–1529) was much more influenced by Buddhism (especially Zen’s Platform Sutra) than has generally been recognized. In light of this influence, and the centrality of questions of selfhood in Buddhism, in this article I will explore the theme of selfhood in Wang’s Neo-Confucianism. Put as a mantra, for Wang “self-awareness is world-awareness.” My central image for this (...)
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  40. How lateral inhibition and fast retinogeniculo-cortical oscillations create vision: A new hypothesis.Jerath Ravinder, Shannon M. Cearley, Vernon A. Barnes & Elizabeth Nixon-Shapiro - 2016 - Medical Hypotheses 96:20-29.
    The role of the physiological processes involved in human vision escapes clarification in current literature. Many unanswered questions about vision include: 1) whether there is more to lateral inhibition than previously proposed, 2) the role of the discs in rods and cones, 3) how inverted images on the retina are converted to erect images for visual perception, 4) what portion of the image formed on the retina is actually processed in the brain, 5) the reason we have an after-image with (...)
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  41. Mad Speculation and Absolute Inhumanism: Lovecraft, Ligotti, and the Weirding of Philosophy.Ben Woodard - 2011 - Continent 1 (1):3-13.
    continent. 1.1 : 3-13. / 0/ – Introduction I want to propose, as a trajectory into the philosophically weird, an absurd theoretical claim and pursue it, or perhaps more accurately, construct it as I point to it, collecting the ground work behind me like the Perpetual Train from China Mieville's Iron Council which puts down track as it moves reclaiming it along the way. The strange trajectory is the following: Kant's critical philosophy and much of continental philosophy which has followed, (...)
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  42. Material souls and imagination in Late Aristotelian embryology.Andreas Blank - 2010 - Annals of Science 67 (2):187-204.
    Summary This article explores some continuities between Late Aristotelian and Cartesian embryology. In particular, it argues that there is an interesting consilience between some accounts of the role of imagination in trait acquisition in Late Aristotelian and Cartesian embryology. Evidence for this thesis is presented using the extensive biological writings of the Padua-based philosopher and physician, Fortunio Liceti (1577–1657). Like the Cartesian physiologists, Liceti believed that animal souls are material beings and that acts of imagination result in material images that (...)
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  43. What Does It Mean to Be Human Today?Julia Alessandra Harzheim - forthcoming - Cambridge Quarterly of Healthcare Ethics.
    With the progress of artificial intelligence, the digitalization of the lifeworld, and the reduction of the mind to neuronal processes, the human being appears more and more as a product of data and algorithms. Thus, we conceive ourselves “in the image of our machines,” and conversely, we elevate our machines and our brains to new subjects. At the same time, demands for an enhancement of human nature culminate in transhumanist visions of taking human evolution to a new stage. Against this (...)
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  44. Rethinking Fetal Personhood in Conceptualizing Roe.Rosemarie Garland-Thomson & Joel Michael Reynolds - 2022 - American Journal of Bioethics 22 (8):64-68.
    In this open peer commentary, we concur with the three target articles’ analysis and positions on abortion in the special issue on Roe v. Wade as the exercise of reproductive liberty essential for the bioethical commitment to patient autonomy and self-determination. Our proposed OPC augments that analysis by explicating more fully the concept crucial to Roe of fetal personhood. We explain that the development and use of predictive reproductive technologies over the fifty years since Roe has changed the literal image, (...)
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  45. Breast Cancer Knowledge Based System.Suheir H. Almurshidi & Samy S. Abu-Naser - 2018 - International Journal of Academic Health and Medical Research (IJAHMR) 2 (12):7-22.
    The Knowledge Based System for Diagnosing Breast Cancer is used to assist medical students to improve their education on diagnosis and counseling the process of analyzing the biopsy image of the microscope, determining the type of tumor and the treatment method for each case and identifying the disease related questions. According to the Ministry of Health in its annual report in Gaza, between 2009 and 2014 there are 7069 cases of breast cancer, and in 2014 there are 1502 cases (...)
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  46. L'etica del Novecento. Dopo Nietzsche.Sergio Cremaschi - 2005 - Roma RM, Italia: Carocci.
    TWENTIETH-CENTURY ETHICS. AFTER NIETZSCHE -/- Preface This book tells the story of twentieth-century ethics or, in more detail, it reconstructs the history of a discussion on the foundations of ethics which had a start with Nietzsche and Sidgwick, the leading proponents of late-nineteenth-century moral scepticism. During the first half of the century, the prevailing trends tended to exclude the possibility of normative ethics. On the Continent, the trend was to transform ethics into a philosophy of existence whose self-appointed task was (...)
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  47. Predictive Modeling of Breast Cancer Diagnosis Using Neural Networks:A Kaggle Dataset Analysis.Anas Bachir Abu Sultan & Samy S. Abu-Naser - 2023 - International Journal of Academic Engineering Research (IJAER) 7 (9):1-9.
    Breast cancer remains a significant health concern worldwide, necessitating the development of effective diagnostic tools. In this study, we employ a neural network-based approach to analyze the Wisconsin Breast Cancer dataset, sourced from Kaggle, comprising 570 samples and 30 features. Our proposed model features six layers (1 input, 1 hidden, 1 output), and through rigorous training and validation, we achieve a remarkable accuracy rate of 99.57% and an average error of 0.000170 as shown in the image below. Furthermore, our investigation (...)
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  48. The Encyclopedia of Neutrosophic Researchers. Volume I.Florentin Smarandache - 2016 - Gallup, NM, USA: Neutrosophic Science International Association.
    This is the first volume of the Encyclopedia of Neutrosophic Researchers, edited from materials offered by the authors who responded to the editor’s invitation. The authors are listed alphabetically. The introduction contains a short history of neutrosophics, together with links to the main papers and books. Neutrosophic set, neutrosophic logic, neutrosophic probability, neutrosophic statistics, neutrosophic measure, neutrosophic precalculus, neutrosophic calculus and so on are gaining significant attention in solving many real life problems that involve uncertainty, impreciseness, vagueness, incompleteness, inconsistent, and (...)
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  49. The Encyclopedia of Neutrosophic Researchers. Volume III.Florentin Smarandache - 2019 - Gallup, NM, USA: Neutrosophic Science International Association.
    This is the third volume of the Encyclopedia of Neutrosophic Researchers, edited from materials offered by the authors who responded to the editor’s invitation. The authors are listed alphabetically. The introduction contains a short history of neutrosophics, together with links to the main papers and books. Neutrosophic set, neutrosophic logic, neutrosophic probability, neutrosophic statistics, neutrosophic measure, neutrosophic precalculus, neutrosophic calculus and so on are gaining significant attention in solving many real life problems that involve uncertainty, impreciseness, vagueness, incompleteness, inconsistent, and (...)
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  50. The Encyclopedia of Neutrosophic Researchers. Volume IV.Florentin Smarandache & Maikel Leyva Vázquez - 2021 - Gallup, NM, USA: Neutrosophic Science International Association.
    This is the fourth volume of the Encyclopedia of Neutrosophic Researchers, edited from materials offered by the authors who responded to the editor’s invitation. The authors are listed alphabetically. The introduction contains a short history of neutrosophics, together with links to the main papers and books. Neutrosophic set, neutrosophic logic, neutrosophic probability, neutrosophic statistics, neutrosophic measure, neutrosophic precalculus, neutrosophic calculus and so on are gaining significant attention in solving many real life problems that involve uncertainty, impreciseness, vagueness, incompleteness, inconsistent, and (...)
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