Parkinson’s Disease Detection Using FMRI Images Leveraging Transfer Learning on Convolutional Neural Network

Parkinson’s disease(PD) is a neurological condition that is dynamic and steadily influences the movement of the human body. PD influences the central apprehensive system which happens because of the hardship of dopaminergic neurons brought about in a neuro-degenerative incubation. The patients who have PD usually suffer from tremor, unyielding nature, postural shifts, and lessen in unconstrained advancements. There is no particular diagnosis process for PD. PD varies from one person to another person depending on the situation and the family history.Magnetic Resonance Imaging (MRI), Computed Tomography (CT), ultrasound of the brain, Positron Emission Tomography (PET) scans are common imaging tests to figure out this disease but these tests are not particularly effective. In this research, several tests are run on two types of data group - control and PD affected people. The dataset is collected from the Parkinson’s Progression Markers Initiative (PPMI) repository. Then MRI slices are processed from the selected data group into the CNN models. Three different Convolutional Neural Network (CNN) architectures are used in this work to extract features from the data group. The CNN models are InceptionV3, VGG16 and VGG19. These models are used in this research to compare and to get better accuracy. Among these models, VGG19 worked best in the dataset because the accuracy for VGG19 is 91.5% where VGG16 gives 88.5% and inceptionV3 gives 89.5% accuracy on detecting PD.