PARKINSON'S DISEASE PREDICTION USING SPIRAL DRAWING IMAGE CLASSIFICATION USING IMPROVED VGG19

Abstract

Motor symptoms greatly diminish the quality of life for those living with Parkinson's disease (PD), a degenerative neurological condition. An early and precise diagnosis is essential for treatment and therapy to be successful. Here, we see a state-of-the-art method for PD prediction based on spiral drawing picture categorization. The preprocessing stage utilizes the Enhanced Residual Noise Elimination Neural Network algorithm, substantially enhancing image quality by effectively reducing unwanted noise. The Improved Mask R-CNN is employed for segmentation, offering superior delineation of relevant structures within the spiral drawings. Feature extraction uses a Convolutional Neural Network (CNN) with AlexNet, which captures intricate patterns and essential details from the images. Finally, classification is performed using IVGG19 Networks, an architecture tailored explicitly for high-precision image classification tasks. Integrating these advanced techniques aims to improve the accuracy of Parkinson's disease prediction, providing a reliable tool for early diagnosis and aiding in better clinical decision-making.