Ensemble deep learning model for early diagnosis and classification of Alzheimer's disease using MRI scans

Abstract

Background

Alzheimer's disease (AD) is an irreversible neurodegenerative disorder characterized by progressive cognitive and memory decline. Accurate prediction of high-risk individuals enables early detection and better patient care.

Objective

This study aims to enhance MRI-based AD classification through advanced image preprocessing, optimal feature selection, and ensemble deep learning techniques.

Methods

The study employs advanced image preprocessing techniques such as normalization, affine transformation, and denoising to improve MRI quality. Brain structure segmentation is performed using the adaptive DeepLabV3 + approach for precise AD diagnosis. A novel optimal feature selection framework, H-IBMFO, integrates the Improved Beluga Whale Optimizer and Manta Foraging Optimization. An ensemble deep learning model combining MobileNet V2, DarkNet, and ResNet is used for classification. MATLAB is utilized for implementation.

Results

The proposed system achieves 98.7% accuracy, with 98% precision, 98% sensitivity, 99% specificity, and 98% F-measure, demonstrating superior classification performance with minimal false positives and negatives.

Conclusions

The study establishes an efficient framework for AD classification, significantly improving early detection through optimized feature selection and deep learning. The high accuracy and reliability of the system validate its effectiveness in diagnosing AD stages.