Parkinson Skin Diseases Detection System using ML

Parkinson’s-related skin conditions can provide early indicators of the neurological disorder, especially through changes in texture, dryness, and seborrheic symptoms. Traditional diagnostic approaches rely on clinical evaluation, specialist analysis, and laboratory tests; typically, they are slow, costly, and reliant on expert availability. Recently, rapid assessment of Parkinson’s abnormalities using images has been enabled by progress in Computer Vision (CV), Deep Learning (DL), Machine Learning (ML), and Artificial Intelligence (AI). This paper offers a thorough examination of AI- based image analysis systems developed for detecting Parkinson’s-related skin abnormalities. The work examines the pre-processing techniques, methods of segmentation, handcrafted and deep feature extraction, and classification. It finds limitations in previous studies, which include datasets collected from realworld and performance degradation due to in uncontrolled environments. The paper also discusses future research directions on lightweight CNN models, IoT-integrated systems, and early-stage disease detection.