Identification of Neurological Disorder Using Deep Learning

Abstract

Neurological diseases that feature conditions like Alzheimer's, Parkinson's, and epilepsy have major impacts on global health and hence underpin a need for high-accuracy early diagnosis. DL has shown considerable success in treating complex data analysis, but challenges remain, like those concerning heterogeneity, limited interpretability, and diagnostic mismatch. Advanced DL architectures, variable and iterative neural networks, and new techniques such as multiple data fusion and translational AI systems provide a sophisticated approach toward improving neurological diagnosis. The aim is to process information handling missing data, improve model robustness, and have physicians who can guide results to inform decision-making. And it is to provide our proposed improvements such that we will improve the accuracy in diagnosis, better improvement in intervention, and higher integration of DL with clinical practice, hence enabling more reliable and accessible vascular health solutions.