A Multimodal Deep Learning Approach for Huntington's disease Detection Using MRI and Genetic Data

Huntington's disease (HD) is a progressive neurodegenerative condition, and early identification using medical imaging is essential for prompt care. This study uses HD Predictor, a recognized medical image classification system that uses MRI imaging and numerical data to improve diagnostic accuracy. The HD Predictor architecture comprises two main pathways: image and metadata processing. In image processing, ResNet-50's last fully connected (FC) layer is replaced with a 128-neuron layer to extract deep features. The metadata processing branch uses a compact feed forward neural network to examine the CAG value via a 16-neuron layer to gather genetic data. Concatenating and processing the obtained information in a final classification layer determines if the brain sample is impaired or healthy. The Cross Entropy loss function is best for multi-class classification. The Adam optimizer, with a learning rate of 0.0001, helps update weights, while the ReduceLROnPlateau scheduler adapts the learning rate to validation findings to increase stability. Training over 30 epochs improves the model's decision-making. By using MRI images and CAG repeat data, HD Predictor outperforms image-based categorization methods. This study emphasizes multimodal data fusion in medical imaging and offers an effective Huntington's disease detection approach.