Authors - S. Jayaraj, G. Anjan Babu, Krishnamurthy Kavitha Abstract - As neurodegenerative diseases like Huntington’s become a global health priority, the difficulty of early and accurate radiological diagnosis remains a significant hurdle. While Deep Learning, predominantly CNNs (Convolutional Neural Networks), offers a clarification for medical image classification, performance is often hindered by the inadequacy of high-grade datasets. This research addresses these limitations by proposing an ensemble deep learning model that integrates ResNet, MobileNet, and VGG16 architectures. By combining these networks, the study achieves enhanced robustness and superior classification accuracy compared to standalone models. This automated framework serves as a vital clinical support tool, enabling faster interventions, improved treatment planning, and a reduction in the global burden of neurodegenerative disorders [10,12].
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