A Comparative Study of Pretrained Deep Neural Networks for Classifying Alzheimer's and Parkinson's Disease

Early detection of neurodegenerative diseases can be challenging, where Deep Learning (DL) techniques have shown promise. Most DL techniques provide a robust and accurate classification performance. However, due to the complex architectures of the DL models, the classification results are difficult to interpret, causing challenges for their adoption in the healthcare industry. To facilitate this, the current work proposes an effective and interpretable analysis pipeline that compares the performances of pre-trained models for the early detection of Alzheimer's Disease (AD) and Parkinson's Disease (PD). The proposed pipeline allows tuning of hyperparameters, such as batch size, number of epochs, and learning rates, to achieve more robust and accurate classification. Additionally, validation of predictions using heatmaps drawn from GradCAM are also provided.