Detection and Prediction of Alzheimer's disease using Deep learning: A review

摘要

It is extremely crucial to get clinically diagnosed for Alzheimer's Disease because it is a progressive brain disease causing complex changes in brain leading to degeneration of cells followed by dementia which gradually exacerbates over time. Dementia can not only cause disabilities in elders but can also be fatal. India has the third-highest caseload of AD, following USA and China, according to international statistics. India's dementia and Alzheimer's burden is forecast to increase exponentially. Many researchers have used various Machine Learning and Deep Learning techniques to detect and predict Alzheimer's. In this review paper, we have analysed and compared the accuracy and the performance of deep learning models such as Recurrent Neural Networks (RNN), Long Short Term Memory (LSTM), Convolutional Neural Networks (CNN), DenseNet, and Graphical Convolutional Neural Networks (GCNN). After roughly comparing, our findings indicate which Deep learning models with which data processing techniques yields a better prediction accuracy.