Classification of Alzheimer’s Disease Using Traditional Classifiers with Pre-Trained CNN

Download Edit this record How to cite View on PhilPapers
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.
PhilPapers/Archive ID
Upload history
First archival date: 2021-05-06
Latest version: 7 (2021-05-12)
View other versions
Added to PP

324 (#25,136)

6 months
163 (#3,102)

Historical graph of downloads since first upload
This graph includes both downloads from PhilArchive and clicks on external links on PhilPapers.
How can I increase my downloads?