Version 1
: Received: 22 December 2023 / Approved: 25 December 2023 / Online: 26 December 2023 (02:49:57 CET)
How to cite:
Afandi, M.; Ardhianto, P.; Lin, J. Diagnosis of Parkinson’s Disease Using Convolutional Neural Network by Hand Drawing Images. Preprints2023, 2023121851. https://doi.org/10.20944/preprints202312.1851.v1
Afandi, M.; Ardhianto, P.; Lin, J. Diagnosis of Parkinson’s Disease Using Convolutional Neural Network by Hand Drawing Images. Preprints 2023, 2023121851. https://doi.org/10.20944/preprints202312.1851.v1
Afandi, M.; Ardhianto, P.; Lin, J. Diagnosis of Parkinson’s Disease Using Convolutional Neural Network by Hand Drawing Images. Preprints2023, 2023121851. https://doi.org/10.20944/preprints202312.1851.v1
APA Style
Afandi, M., Ardhianto, P., & Lin, J. (2023). Diagnosis of Parkinson’s Disease Using Convolutional Neural Network by Hand Drawing Images. Preprints. https://doi.org/10.20944/preprints202312.1851.v1
Chicago/Turabian Style
Afandi, M., Peter Ardhianto and Jun-Dai Lin. 2023 "Diagnosis of Parkinson’s Disease Using Convolutional Neural Network by Hand Drawing Images" Preprints. https://doi.org/10.20944/preprints202312.1851.v1
Abstract
Neurodegenerative illnesses, such as Parkinson’s disease (PD), have a substantial impact on the overall well-being of those who are affected. This study investigates and contrasts the capabilities of convolutional neural networks (CNN) in detecting Parkinson’s disease (PD) by utilising hand-drawn images alongside wave and spiral images as input data. This study employs pre-trained CNN models, specifically MobileNet, ResNet50, EfficientNet-B1, and InceptionV3, to classify Parkinson’s disease (PD). The findings demonstrate that MobileNet surpasses other architectural designs, as evidenced by the F1-Score of the four classes: Spiral Normal (0.87), Spiral Parkinson (0.86), Wave Normal (0.97), and Wave Parkinson (0.97). MobileNet has also shown a remarkable accuracy of 0.92 in diagnosing Parkinson’s disease. The result demonstrates the efficacy of MobileNet in extracting features from images. The results of this study enhance the application of deep learning methods in the early detection of PD, as well as help indicate the effectiveness of patient therapy and exercise, promising better patient outcomes through timely intervention and treatment.
Keywords
parkinson; deep learning; classification; mobilenet; neurodegenerative
Subject
Public Health and Healthcare, Nursing
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
