Deep Learning Augmented with SMOTE for Timely Alzheimer's Disease Detection in MRI Images

Abstract

Timely diagnosis of Alzheimer's Disease (AD) is pivotal for effective intervention and improved patient outcomes, utilizing Magnetic Resonance Imaging (MRI) to unveil structural brain changes associated with the disorder. This research presents an integrated methodology for early detection of Alzheimer's Disease from Magnetic Resonance Imaging, combining advanced techniques. The framework initiates with Convolutional Neural Networks (CNNs) for intricate feature extraction from structural MRI data indicative of Alzheimer's Disease. To address class imbalance in medical datasets, Synthetic Minority Over-sampling Technique (SMOTE) ensures a balanced representation of Alzheimer's Disease and non- Alzheimer's Disease instances. The classification phase employs Spider Monkey Optimization (SMO) to optimize model parameters, enhancing precision and sensitivity in Alzheimer's Disease diagnosis. This work aims to provide a comprehensive approach, improving accuracy and tackling imbalanced datasets challenges in early Alzheimer's detection. Experimental outcomes demonstrate the proposed approach outperforming conventional techniques in terms of classification accuracy, sensitivity, and specificity. With a notable 91% classification accuracy, particularly significant in medical diagnostics, this method holds promise for practical application in clinical settings, showcasing robustness and potential for enhancing patient outcomes in early-stage Alzheimer's diagnosis. The implementation is conducted in Python.