Deep Neural Network-Based Classification and Diagnosis of Idiopathic Parkinsonism Disease

Present days deep neural networks play a crucial role in the prediction and classification of diseases. Without a doubt, DNN has a promising future in the medical area, particularly in clinical imaging. The fame of profound learning approaches is a result of their capacity to deal with a lot of information identified with the patients with reliability, accuracy in a limited ability to focus time. Nonetheless, the specialists might set aside time in breaking down and produce reports. In this work, have proposed a Deep Neural Network-based Parkinson's disease classification (DPDC). Our proposed technique is one such genuine model giving quicker and more precise outcomes for the characterization of Parkinson's sickness patients with magnificent accuracy of 94.87%. Because of the traits of the dataset of the patient, the model can be utilized for the recognizable proof of Parkinsonism's.