Harnessing transfer learning for Alzheimer's disease prediction

Nowadays, Alzheimer's Disease (AD) has become a massive problem for middle-aged and older adults. Although due to its long incubation period and early mild symptoms, patients have a more extended period and more possibilities to check out, it is still hard for patients and doctors to diagnose in early routine examinations. This article provides a new method to help the doctor to diagnose Alzheimer's Disease in the early phase. We use transfer learning in deep learning to help diagnose Alzheimer's Disease early in developing Computed Tomography (CT) brain images. Using three pre-trained models, ShuffleNet, DenseNet, and NASNet-mobile as the transfer learning training model and convolution neural networks. We made some improvements to make it more relevant to the actual situation. DenseNet has best performance (87.36%) among the three models. We set the output into four classes: the four stages of Alzheimer's are widely recognized (Mild Demented, Moderate Demented, Very Mild Demented).