Diagnosing Alzheimer's Disease Based on Multiclass MRI Scans using Transfer Learning Techniques

Aims: To prevent Alzheimer's disease (AD) from progressing to dementia, early predic-tion and classification of AD are important and they play a crucial role in medical image analysis. Background: In this study, we employed a transfer learning technique to classify magnetic reso-nance (MR) images using a pre-trained convolutional neural network (CNN). Objective: To address the early diagnosis of AD, we employed a computer-assisted technique, spe-cifically the deep learning (DL) model, to detect AD. Methods: In particular, we classified Alzheimer's disease (AD), mild cognitive impairment (MCI), and normal control (NC) subjects using whole slide two-dimensional (2D) images. To illustrate this approach, we made use of state-of-the-art CNN base models, i.e., the residual networks Res -Net-101, ResNet-50, and ResNet-18, and compared their effectiveness in identifying AD. To evalu-ate this approach, an AD Neuroimaging Initiative (ADNI) dataset was utilized. We also showed uniqueness by using MR images selected only from the central slice containing left and right hippo-campus regions to evaluate the models. Results: All three models used randomly split data in the ratio of 70:30 for training and testing. Among the three, ResNet-101 showed 98.37% accuracy, better than the other two ResNet models, and performed well in multiclass classification. The promising results emphasize the benefit of us-ing transfer learning, specifically when the dataset is low. Conclusion: From this study, we know that transfer learning helps to overcome DL problems main-ly when the data available is insufficient to train a model from scratch. This approach is highly ad-vantageous in medical image analysis to diagnose diseases like AD.