Abstract
Parkinson’s Disease (PD) is a neurodegenerative disorder that affects numerous people and tends to get more acute as time progresses. From its early stages, several symptoms occur among patients such as micro-graphing, tremors, and stiffness. If identified beforehand, diagnosis is much more effective. This work aims to build an automated deep learning-based system to determine whether a given individual is suffering from Parkinson’s. We utilize images of written exams (from the HandPd dataset, consisting of Spiral and Meander templates) taken by subjects for this very purpose. Physiological datasets are challenging to work with due to typical obstacles associated with them, such as insufficient data and disproportionate class representation. The proposed methodology employs techniques intuitively based on Transfer Learning to solve the mentioned problems. Through these procedures, an accuracy of 98.24% on the Spiral dataset and 98.11% on the Meander dataset was achieved.
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Awatramani, V., Gupta, D. (2021). Parkinson’s Disease Detection Through Visual Deep Learning. In: Gupta, D., Khanna, A., Bhattacharyya, S., Hassanien, A.E., Anand, S., Jaiswal, A. (eds) International Conference on Innovative Computing and Communications. Advances in Intelligent Systems and Computing, vol 1166. Springer, Singapore. https://doi.org/10.1007/978-981-15-5148-2_83
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DOI: https://doi.org/10.1007/978-981-15-5148-2_83
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