A Deep Learning Approach With Sparse Autoencoder for Alzheimers Disease Classification

Alzheimer's Disease is a progressive neurodegenerative condition distinguished by a steady deterioration in cognitive abilities. Given the empirical inquiries conducted, it is imperative to take age into account as a fundamental criterion when selecting participants. Younger individuals are more vulnerable to the transient aspect compared to those who are older. The current work focused on selecting young-onset subjects, as early diagnosis can significantly impact patients' lives. The identification of this disease at an early stage using traditional means poses significant difficulties. Deep Learning (DL) has emerged as a highly effective approach for enhancing the performance of diagnostic processes and boosting forecast accuracy. The research utilized deep learning techniques and neuroimaging approaches to autonomously identify and categorize the condition based on its phases of mild cognitive impairment. The suggested study is conducted through a series of three sequential phases. Prior to further analysis, the 3D input visuals need to undergo image preprocessing with Weiner filtering in order to remove noise and smooth the image. Following, Transfer learning models are utilized to extract features, which are then compressed through the use of cascaded Auto Encoders. The ultimate stage involves the utilization of a fine tuned Deep Neural Network (DNN) for categorizing of the phases of AD into five classes. The combination of the ResNet-18 and sparse autoencoder with the deep neural network model yielded a remarkable accuracy rate of 95.62%.