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

Aims To prevent Alzheimer's disease (AD) from progression to dementia, early prediction and classification of AD plays a crucial role in medical image analysis. Background In this study, we employed transfer learning technique to classify Magnetic Resonance (MR) images using a pre-trained convolutional neural network (CNN). Objective To address the early diagnosis of AD, we employed computer-assisted technique specifically 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 ResNet-101, ResNet-50 and ResNet-18, and compared their effectiveness to identifying AD. To evaluate this approach, an AD Neuroimaging Initiative (ADNI) dataset was utilized. We have also showed uniqueness by using MR images selected only from the central slice containing left and right hippocampus regions to evaluate the models. Results All the three models used randomly split data in the ratio 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 using transfer learning specifically when the dataset is low. Conclusion From this study, we can assure that transfer learning helps to overcome DL problems mainly when the data available is insufficient to train a model from scratch. This approach is highly advantageous in medical image analysis to diagnose diseases like AD.