Authors - Poonam Yadav, Meenu Vijarania, Meenakshi Malik, Neha Chhabra, Ganesh Kumar Dixit Abstract - Parkinson's disease is aging-associated degenerative brain illness that results in the degeneration of certain brain regions. Early medical diagnosis of Parkinson's disease (PD) is difficult for medical professionals to make with precision. Magnetic Resonance Imaging (MRI) and single-photon emission computed tomography, or SPECT are two medical imaging strategies that can be used to non-invasively and safely assess quantitative aspects of brain health. Strong machine learning and deep learning methods, along with the efficiency of medical imaging techniques for evaluating neurological wellness, are necessary to accurate the identification of Parkinson's disease (PD). In this study, we have used dataset of MRI images. This study suggests three deep learning models: ResNet 50, MobileNetV2 and InceptionV3 for early diagnosis of PD utilizing MRI database. From these three models, MobileNetV2 demonstrated superior accuracy in training, testing and validation with a rate of 99%, 94%, and 96%, respectively. With its effectiveness and precision, MobileNet V2 has a lot of potential for PD identification using MRI scans in the future. We may further advance the development of dependable and easily accessible AI-powered solutions for early diagnosis and better patient care by tackling the issues and investigating the above-mentioned future paths.
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