Abstract
Parkinson's disease is a neurodegenerative disorder that develops in an individual when the required amount of dopamine is not produced by respective neurons. The most common symptoms of this disease are tremors or shaking in hand/arms, changes in handwriting, muscle stiffness, slowness during walking, and change in speech. It is an incurable disease, but managing its symptoms can delay its progression, which is possible only if it is diagnosed at an early stage. Prior research work had proved that variation in handwriting can be considered as a quantitative marker for Parkinson's disease diagnosis. The authors present an automated Parkinson's diagnosis system using transfer learning techniques. The performance of the presented system is analyzed by using Parkinson’s spiral drawing dataset. Four transfer learning architectures ResNet 34, DensNet 121, VGG 16, and AlexNet are used to classify spiral images of Parkinson’s patients and healthy individuals. The performance of these architectures was examined in terms of accuracy, sensitivity, specificity, and ROC-AUC. After fine-tuning, it was noted that the performance of all architectures improved and the AlexNet architecture outperformed with 93.33% accuracy and 0.96 AUC.
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Lamba, R., Gulati, T., Jain, A. (2022). Automated Parkinson’s Disease Diagnosis System Using Transfer Learning Techniques. In: Marriwala, N., Tripathi, C.C., Jain, S., Mathapathi, S. (eds) Emergent Converging Technologies and Biomedical Systems . Lecture Notes in Electrical Engineering, vol 841. Springer, Singapore. https://doi.org/10.1007/978-981-16-8774-7_16
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