EARLY PARKINSON'S DISEASE IDENTIFICATION FROM BRAIN MRI IMAGES USING DEEP LEARNING

ABSTRACTAlzheimer's disease (PD), which significantly impacts The calibre of life for millions ofelderly adults has become a major disorder that affects the brain and spinal cord globally..Effective therapy and oversight of PD rely going early detection and precise diagnosis. However,diagnosing PD has been challenging due to its similarities with other neurological disorders,leading to a 25% rate of faulty manual diagnoses.Magnetic Resonance Imaging (MRI) of the brain has demonstrated significant promise inidentifying and diagnosing Parkinson's disease. This research suggests utilizing neural networkswith convolutions (CNNs), a form of profound learning model, to distinguish Parkinson's illnessand differentiate between affected individuals and healthy subjects.. The study utilizes theInitiative for Parkinson Progression Markers (PPMI) dataset for classification.The Parkinson’s disease recognition system leverages CNN to analyze the images. Theeffectiveness of The suggested approach isassessed in light of measurements such as accuracy, particularity, sensitivity, as well as theArea Under the Curve (AUC). Keyword: - An area under the curve (AUC) with convolutional neural networks (CNN)), and magneticresonance imaging (MRI).