Enhancing Alzheimer’s Disease Detection Using MRI Scans Through Transfer Learning Approach

Abstract

Alzheimer's Disease (AD) is a pervasive neurodegenerative ailment, affecting a vast population globally and posing challenges for early and precise diagnosis within medical image analysis. Although machine learning and deep learning have emerged as competent methodologies for AD detection, several obstacles persist, especially with imbalanced datasets and convolutional effectiveness. This research thesis is using deep learning models empowered with transfer learning to efficiently detect the Alzheimer’s disease classes. More precisely, by employing fine tuning a pre-trained VGG16 and Inception V3 model is investigated for multi-class classification. The study's paramount objective is to enhance AD detection via a custom fine-tuning framework. In this study CNN deep learning models VGG 16 (sequence 1) and Inception V3 (sequence 1 & 2) are proposed for classifying AD in to four stages i.e. Non-demented, Very Mild-demented, Mild-demented and Moderate-demented using brain MRI scans. Then, their performance is evaluated using certain metrics such as accuracy, loss, precision, recall, f1-score, Matthew’s correlation coefficient and balanced accuracy. The results showed that the proposed models, VGG16 (sequence 1) and Inception V3 (sequence 1 & 2) outperformed many of the state-of-the-art models by achieving testing accuracies of 93.9% and (93.87%, 93.09%) for Kaggle MRI dataset.