Design and Evaluation of an Interpretable Multimodal Deep Learning Framework for Early Alzheimer’s Disease Detection
DOI:
https://doi.org/10.66834/798kjf21Keywords:
Alzheimer’s Disease, CT Scan Classification, EfficientNetV2-S, Deep Learning, AI Chatbot, Medical Imaging, Transfer LearningAbstract
Alzheimer’s disease is a progressive neurodegenerative disorder that significantly impairs memory and cognitive functions and affects over 55 million people worldwide. The successful management and planning require early and accurate diagnosis. Conventional radiological assessment is often subjective and time-consuming, which highlights the need for automated and reliable diagnostic solutions. Most deep learning models show promise for classifying neuroimaging data, but they tend to be less computationally efficient and less interpretable, and they cannot be integrated into patient-centric processes. The gap between developing diagnostic algorithms with high accuracy and implementing them in a supportive framework that includes patients and caregivers is large. This paper introduces a comprehensive, hybrid framework that addresses these gaps. We present a dual-modality diagnostic system: a deep learning pipeline using EfficientNetV2-S for CT scan classification, complemented by a Feedforward Neural Network (FNN) that analyses structured clinical data for holistic patient assessment. This diagnostic core is integrated into a user-friendly graphical user interface (GUI) and supplemented by ”NeuroBot,” an AI-powered chatbot that provides domain-specific information and support. The two models have been trained using the transfer learning method on a curated dataset of 30,000 brain CT slices. The EfficientNetV2-S model achieved an accuracy of 98.19%. After hyperparameter tuning, the FNN model achieved an optimised accuracy of 87.21%. The importance of the features addressed by the models was proved with the help of the statistical t-tests of the corresponding clinical data. The integrated system enables a scalable, translatable, and patient-centered system to improve the early analysis and treatment of Alzheimer’s disease.
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