CLASSIFICATION OF ALZHEIMER'S DISEASE USING CONVOLUTIONAL NEURAL NETWORKS
Samhan, Lamis F., Alfarra, Amjad H., Abu-Naser, Samy S.
Abstract
Due to their sensitivity, the difficulty of executing surgeries, and other factors, disorders related to the brain are among the most difficult diseases to treat and their hefty prices. Contrarily, since the procedure's outcomes are not guaranteed to be successful, it is not necessary for the operation to be successful. One Alzheimer's disease, which affects adults and causes memory loss, is one of the most prevalent conditions that damage the brain and various degrees of information forgetting. based on each patient's condition. For these reasons, it's crucial to identify memory loss, determine the patient's severity of cognitive impairment, and determine the patient's diagnosis of Alzheimer's disease by brain CT scans. In this thesis, we explore methods and techniques for using deep learning classification to categorize Alzheimer's disease. In large studies, this strategy is utilized to improve patient care, save expenses, and allow for speedy and reliable analysis. The model will be created. The Python programming language was utilized to construct the system, which is particularly valuable for clinicians in identifying Alzheimer's disease. Our trained model got a 100% accuracy by using 70% of the image for training and 30% for validation on a stalled test set.