A survey of machine learning applications in medical imaging for neurodegenerative disease diagnosis

The increasing prevalence of brain degenerative disorders particularly Alzheimer's and Parkinson's, have incurred a dire need for effective diagnostic methodologies. With advancements in medical imaging techniques and Artificial Intelligence (AI), transformative potential exists to enhance early detection and disease classification. This review examines the recent progress made in the use of diagnostic medical imaging and artificial intelligence (AI) techniques for the early detection of Alzheimer's disease and Parkinson's disease. Despite significant progress, a comprehensive overview is lacking. The review aims to summarize and synthesize existing methods, critically evaluate their strengths and limitations, and identify emerging trends. Multimodal imaging data, combining information from different sources like MRI, PET, and SPECT, has shown progress in improving diagnostic accuracy and providing insights into disease progression. Advanced AI techniques, such as transfer learning and generative adversarial networks, have further enhanced the performance of diagnostic models. Previous studies have predominantly focused on using standalone techniques, often leaving the integration of multi-modal imaging and deep learning methodologies that could lead to more robust diagnostic frameworks. This literature review addresses this gap by synthesizing current research on medical imaging modalities, AI applications, and their implications in diagnosing Alzheimer's and Parkinson's diseases. This review seeks to connect technological innovations with clinical applications to establish more robust and dependable diagnostic methods.