Patients with Parkinson's disease (PD) often manifest motor dysfunction symptoms, including tremors and stiffness. The presence of these symptoms may significantly impact the handwriting and sketching abilities of individuals during the initial phases of the condition. Currently, the diagnosis of PD depends on several clinical investigations conducted inside a hospital setting. One potential approach for facilitating the early identification of PD within home settings involves the use of hand-written drawings inside an automated PD detection system for recognition purposes. In this study, the PD Spiral Drawings public dataset was used for the investigation and diagnosis of PD. The experiments were conducted alongside a comparative analysis using 204 spiral and wave PD drawings. This study contributes by conducting deep learning models, namely DenseNet201 and VGG16, to detect PD. The empirical findings indicate that the DenseNet201 model attained a classification accuracy of 94% when trained on spiral drawing images. Moreover, the model exhibited a receiver operating characteristic (ROC) value of 99%. When comparing the performance of the VGG16 model, it was observed that it attained a better accuracy of 90% and exhibited a ROC value of 98% when trained on wave images. The comparative findings indicate that the outcomes of the proposed PD system are superior to existing PD systems using the same dataset. The proposed system is a very promising technological approach that has the potential to aid physicians in delivering objective and dependable diagnoses of diseases. This is achieved by leveraging important and distinctive characteristics extracted from spiral and wave drawings associated with PD.
Citation: Theyazn H. H. Aldhyani, Abdullah H. Al-Nefaie, Deepika Koundal. Modeling and diagnosis Parkinson disease by using hand drawing: deep learning model[J]. AIMS Mathematics, 2024, 9(3): 6850-6877. doi: 10.3934/math.2024334
Patients with Parkinson's disease (PD) often manifest motor dysfunction symptoms, including tremors and stiffness. The presence of these symptoms may significantly impact the handwriting and sketching abilities of individuals during the initial phases of the condition. Currently, the diagnosis of PD depends on several clinical investigations conducted inside a hospital setting. One potential approach for facilitating the early identification of PD within home settings involves the use of hand-written drawings inside an automated PD detection system for recognition purposes. In this study, the PD Spiral Drawings public dataset was used for the investigation and diagnosis of PD. The experiments were conducted alongside a comparative analysis using 204 spiral and wave PD drawings. This study contributes by conducting deep learning models, namely DenseNet201 and VGG16, to detect PD. The empirical findings indicate that the DenseNet201 model attained a classification accuracy of 94% when trained on spiral drawing images. Moreover, the model exhibited a receiver operating characteristic (ROC) value of 99%. When comparing the performance of the VGG16 model, it was observed that it attained a better accuracy of 90% and exhibited a ROC value of 98% when trained on wave images. The comparative findings indicate that the outcomes of the proposed PD system are superior to existing PD systems using the same dataset. The proposed system is a very promising technological approach that has the potential to aid physicians in delivering objective and dependable diagnoses of diseases. This is achieved by leveraging important and distinctive characteristics extracted from spiral and wave drawings associated with PD.
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Theyazn H. H. Aldhyani, Abdullah H. Al-Nefaie, Deepika Koundal. Modeling and diagnosis Parkinson disease by using hand drawing: deep learning model[J]. AIMS Mathematics, 2024, 9(3): 6850-6877. doi: 10.3934/math.2024334
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