Deep Transfer Learning Based Diagnosis of Multiple Neurodegenerative Disorders

Neurodegenerative disorders challenge healthcare systems globally, impacting millions of lives and diminishing quality of life. Machine learning, particularly deep learning, offers promise in improving disease classification. Using a two-step methodology, This study looks into classifying three distinct neurodegenerative diseases using transfer learning: cerebral ataxia, Alzheimer's disease, and Parkinson's disease. First, we use neural network architectures that have already been trained, including VGG16 and ResNet101, which were first trained on large image datasets. This enables us to extract high-level features that capture important disease patterns and characteristics from medical images. By adapting these pre-trained networks to the unique attributes of each disease, we enhance the model's ability to discriminate between them effectively. We use optimization techniques like SGD, Rmsprop, and Adam algorithm to get the optimized results, we employ a range of evaluation metrics, including accuracy and loss. These metrics provide a comprehensive understanding of the model's competence in accurately classifying patients with diverse neurodegenerative diseases. To make our model more accessible and user-friendly, we deploy it through the Flask web framework. Our research findings conclusively demonstrate that the amalgamation of transfer learning, Flask deployment, and stringent evaluation metrics substantially enhances the accuracy of neurodegenerative disease classification when compared to traditional diagnostic methods. This novel strategy promises to transform the identification and management of neurodegenerative illnesses, resulting in more prompt and efficient patient interventions.