Implementation of Deep Learning Approaches for Early Detection of Parkinson's Disease From MRI Images

Abstract

Accurately detecting Parkinson's disease (PD) at an early stage is certainly indispensable for slowing down its progress and providing patients the possibility of accessing disease-modifying therapy. The premotor stage in PD should be prudently checked for the early detection of PD. An innovative deep-learning technique is introduced to quickly uncover whether an individual is in the mild motor impairment stage of Parkinson's disease. The mild motor impairment stage is the pre-motor stage of Parkinson's disease. The pre-motor stage of Parkinson's disease is detected by applying an end-to-end deep learning model. Implementing and optimizing the algorithm of an embedded platform is crucial. Literature shows that NVIDIA Jetson is an exemplar embedded system; many of the ideas and optimizations will apply just as well to existing and future embedded systems. It is widely believed that the ability to run AI algorithms on low-cost, low-power platforms will be crucial for achieving advances in biomedical engineering.