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Magnetic Resonance Imaging (MRI) is a medical imaging method used to visualize the brain’s anatomy, evaluate its function, and identify any abnormalities or disorders without the need for surgical intervention. Parkinson’s Disease (PD) is a condition of gradual nervous decline that effects the neurological system and the bodily functions regulated by the nerves. The impact on Quality of Life (QOL) is significant, resulting in stigma, deterioration of cognitive function, and increased limitations in mobility, including activities of daily living. Hence, early-stage diagnosis and classification of PD is crucial. This study introduces a new Deep Neural Network architecture, designed by combining the LeNet and U-Net models (LUNet) with added attention and/or residual modules for the identification of PD. The MR Images underwent pre-processing and augmentation to facilitate the precise and efficient training of Deep Learning (DL) models. The proposed model was trained using 2000 enhanced images, while validation and testing was conducted on a set of 500 untrained data. The final model is assessed using various statistical evaluation metrics and compared with LeNet-5, U-Net model along with its variants and existing works. The overall accuracies of LeNet, U-Net, and the Proposed model were 95.92 %, 97.6 %, and 99.58 % respectively. |
