An Efficient Approach Based on Medical Images for Parkinsons Disease Identification and Management

Abstract: A progressive neurodegenerative condition that profoundly affects motor and cognitive abilities is Parkinson's disease (PD). Timely action and improved patient outcomes depend on an early and correct diagnosis. Two different models are used in this study's deep learning-based method for Parkinson's disease detection: Long Short-Term Memory (LSTM) networks for structured patient data classification and Convolutional Neural Networks (CNN) for handwriting picture analysis. In order to uncover patterns of motor impairment, the CNN model uses spatial features extracted from handwriting images, and the LSTM model analyses sequential patient data to find PD-related distinctive trends. Handwriting samples from both healthy and Parkinson's patients are included in the dataset, as are organized medical records with motor and cognitive test results.To improve model performance, preprocessing methods such data standardization, normalization, and image scaling are used.