Design of An Iterative End-to-End Multi-Modal Deep Learning Framework for Explainable Diagnosis of Alzheimer’s and Parkinson’s Diseases from Brain Imaging Process

  • Authors

    • Swati K. Mohod Research Scholar, Department of Electrical Engineering, Yashwantrao Chavan College of Engineering, Nagpur, India
    • Rajesh Thakare Department of Electrical Engineering, Yashwantrao Chavan College of Engineering, Nagpur, India
    https://doi.org/10.14419/damxzp29

    Received date: July 14, 2025

    Accepted date: August 1, 2025

    Published date: August 12, 2025

  • Neurodegenerative Diagnosis, Multi-Modal Imaging, Deep Learning, Alzheimer’s Disease, Parkinson’s Disease, Scenarios

  • Abstract

    The precise and early diagnosis of neurodegenerative diseases such as Alzheimer's Disease (AD) and Parkinson's Disease (PD) continues to pose a critical clinical challenge due to common symptoms as well as the progressive nature of these diseases. Most of the currently existing systems are characteristically mono-modal imaging or handcrafted features; hence their generalizability, robustness, and interpretability get limited. Moreover, the current models do not form any unified chain comprising preprocessing, region segmentation, feature fusion, classification, and explainability; this limits their deployment in real clinical settings. To address these challenges, this work proposes an integrative, end-to-end multi-modal diagnostic framework tailored for AD and PD detection using MRI, CT, and X-ray brain images and samples. Starting with Contrast Limited Adaptive Histogram Equalization (CLAHE) for image enhancement, it improves contrast and noise reduction. UNet++ is used in Region of Interest (ROI) segmentation targeting disease relevant locations, such as the hippocampus and basal ganglia. EfficientNet-B7 provides robust representation learning by extracting high dimensional embeddings from ROI-masked images, utilizing pretrained weights with medically fine-tuned retrievals. A novel use of a multi-head self-attention mechanism is then employed to detect features between modalities for best cross-modal integration. A CNN-Transformer hybrid model that effectively combines local spatiality and a global context awareness is employed to classify the data samples. Grad-CAM++ yields high-fidelity saliency maps for model explainability that closely aligns with radiologist annotations. High-performance metrics (Accuracy ≥ 95%, Sensitivity ≥ 92%, Specificity ≥ 93%) are attained by this system but also provide interpretable outputs and actionable insights in processing. The integration of more advanced deep learning modules within one pipeline marks a significant step towards a reliable, explainable, and clinically proven diagnosis of AD and PD Sets.

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  • How to Cite

    Mohod, S. K. . ., & Thakare , R. . (2025). Design of An Iterative End-to-End Multi-Modal Deep Learning Framework for Explainable Diagnosis of Alzheimer’s and Parkinson’s Diseases from Brain Imaging Process. International Journal of Basic and Applied Sciences, 14(SI-2), 148-156. https://doi.org/10.14419/damxzp29