Diagnosis of Alzheimer's Disease Using Deep Learning Approach

Artificial Intelligence can aid in the early diagnosis of chronic illnesses like Alzheimer's disease. Machine learning is being used to help in the identification of Alzheimer's disease in its early stages. AI has a lot of potential applications in the field of healthcare. Researchers explored various machine learning and deep learning classifiers for predicting Alzheimer's Disease (AD). Among them, the Convolutional Neural Network (CNN) model emerged as the champion, achieving an impressive accuracy on a binary classification dataset. This study assesses the diagnostic performance of Convolutional Neural Networks (CNN) and ResNet50 on 6,400 pre-processed MRI scans. Early detection of Alzheimer's disease is being aided by machine learning, and the CNN model has proven to be the most successful in this regard, with an astounding 93 % accuracy rate. This remarkable performance implies that CNNs have enormous promises for detecting AD at an early stage, enabling doctors and clinicians to intervene sooner and offer better treatment alternatives. The study does, however, recognize that class imbalance poses a significant challenge to ResNet50 and results in significantly reduced accuracy. This will be addressed in future research projects by adding a bigger and more reliable collection of highquality images and fixing ResNet50's feature extraction issues and class imbalance. By advancing the development of reliable AI-driven diagnostic tools for the early and precise identification of Alzheimer's disease, these developments have the potential to completely transform clinical practice and patient care.