Implementation of Deep Learning System for the Detection and Identification of Neurological Illness

Abstract: Millions of people around the globe are impacted by neurological disorders including Alzheimer’s and Parkinson’s, epilepsy and multiple sclerosis, and they have a significant impact on the patients’ quality of life. Early and proper diagnosis is very important in enhancing the treatment outcomes. This study presents a deep learning-based system to automatically detect and identify neurological diseases of the brain imaging images and clinical data. The system combines convolutional neural networks (CNN) with feature extraction and recurrent neural networks (RNN) with time data analysis, and its accuracy was 94.2% when it was used in classification tasks on several datasets. A comparative study of the traditional machine learning models shows better sensitivity and robustness. The research helps in improving the efficiency of diagnosis in healthcare systems, particularly in resource constrained systems.

Keywords: Deep Learning, Neurological Disorders, Brain Imaging, CNN, RNN, Feature Extraction, Medical Diagnosis.