Development of a Deep Learning Model for the classification of Alzheimer’s Disease from Magnetic Resonance Imaging

Abstract

Alzheimer’s disease (AD) is a disorder in which the nervous system slowly declines progressively which affects reasoning, forgetting, balancing, daily activities and memory. The early recognition and diagnosis help to manage and treat the disease effectively. Magnetic Resonance Imaging (MRI) especially 3D scan brain imaging which provides detail structural format and information that can aid in recognizing anomalies linked to the various stages of Alzheimer’s disease (AD). The problem face with manual interpretation of MRI scans is enormous in terms of accuracy and time consuming with clinical experts. In this study, we propose a deep learning approach for the multi classification of Alzheimer’s disease from 3D MRI images. The framework uses Convolutional Neural Networks (CNNs) for developing intelligent model for effective 3D image analysis and interpretation. To enhance classification performance, the extracted region of interest is modified with deep learning classifiers including Efficient Net, SE-ResNet and Dense Net. These architectures improve feature representation, enhance efficiency and improving learning capability of the framework. The results shows that the model achieves accuracy of 83% and precision of 82%, which indicates strong performance. The recall and F1-sore display a balance of 81 % across ford in a distinct phase in the progression of Alzheimer’s disease multi class classification. This model will assist the clinicians and radiologist in early interpretation, detection, diagnosis and monitoring of Alzheimer’s disease progression.