Alzheimer Disease Detection Using Advanced Transfer Learning Techniques on MRI Images

Alzheimer's disease (AD) is a neurological disorder that has a profound impact on millions of individuals globally. It is imperative to identify and diagnose AD at an early stage with precision, as this plays a crucial role in timely intervention and effective treatment. In recent years, the utilization of magnetic resonance imaging (MRI) in conjunction with deep learning has demonstrated promising results for assisting in the detection of Alzheimer's disease (AD). This study introduces a methodology for detecting AD by employing MRI images and convolutional neural networks (CNNs). Five pre-trained CNN models, including MobileNetV2, ResNet50, EfficientNet-B0, InceptionV3, and DenseNet-201, are employed for the transfer learning (TL) task. A comparative study and detailed analysis of the performances achieved by each model are presented. The models are trained on a large dataset of MRI images, consisting of both healthy and AD-affected brains with hyper-parameters tuning. During training, the CNNs learn to automatically identify patterns and discriminative features that differentiate between normal and AD-affected brain regions. To evaluate the effectiveness of the proposed method, a comprehensive set of experiments is conducted using publicly available datasets. From the experimental study, it is observed that the TL approach using the five pretrained CNN models achieves good performances in the two class classification problem of Very Mild Demented and Non Demented individuals. The results of this paper contribute to the growing field of computer-aided diagnosis for AD and emphasize the effectiveness of deep learning techniques, particularly CNNs, in the analysis of medical imaging data.