Ensemble deep learning model for early diagnosis and classification of Alzheimer's disease using MRI scans.
Authors
Affiliations (4)
Affiliations (4)
- Department of Biomedical Instrumentation Engineering, Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore, India.
- Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Guntur, Andhra Pradesh, India.
- Department of Electrical and Electronics Engineering, Saveetha Engineering College, Chennai, India.
- Department of Electronics and Communication Engineering, Vel Tech Rangarajan, Dr. Sagunthala R&D Institute of Science and Technology, Chennai, India.
Abstract
BackgroundAlzheimer'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.ObjectiveThis study aims to enhance MRI-based AD classification through advanced image preprocessing, optimal feature selection, and ensemble deep learning techniques.MethodsThe 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.ResultsThe 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.ConclusionsThe 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.