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ORIGINAL RESEARCH article
Front. Med.
Sec. Precision Medicine
Volume 11 - 2024 |
doi: 10.3389/fmed.2024.1453743
This article is part of the Research Topic Cluster-based Intelligent Recommendation System for Hybrid Healthcare Units View all 19 articles
Utilizing Deep Learning Models in an Intelligent Spiral Drawing Classification System for Parkinson's Disease Classification
Provisionally accepted- Department of Health Informatics, College of Health Sciences, Saudi Electronic University, Dammam, Saudi Arabia
Parkinson's disease (PD) is a neurodegenerative illness that impairs normal human movement. The primary cause of PD is the deficiency of dopamine in the human brain. PD also leads to several other challenges, including insomnia, eating disturbances, excessive sleepiness, fluctuations in blood pressure, sexual dysfunction, and other issues. The suggested system is an extremely promising technological strategy that may help medical professionals provide accurate and unbiased disease diagnoses. This is accomplished by utilizing significant and unique traits taken from spiral drawings connected to Parkinson's disease. While PD cannot be cured, early administration of drugs may significantly improve the condition of a patient with PD. An expeditious and accurate clinical classification of PD ensures that efficacious therapeutic interventions can commence promptly, potentially impeding the advancement of the disease and enhancing the quality of life for both patients and their caregivers. Transfer learning models have been applied to diagnose PD by analyzing important and distinctive characteristics extracted from hand-drawn spirals. The studies were carried out in conjunction with a comparison analysis employing 102 spiral drawings. This work enhances current research by analyzing the effectiveness of transfer learning models, including VGG19, InceptionV3, ResNet50v2, and DenseNet169, for identifying PD using handdrawn spirals. Transfer machine learning models demonstrate highly encouraging outcomes in providing a precise and reliable classification of PD. Actual results demonstrate that the InceptionV3 model achieved a high accuracy of 89% when learning from spiral drawing images and had a superior receiver operating characteristic (ROC) curve value of 95%. The comparison results suggest that PD identification using these models is currently at the forefront of PD research. The dataset will be enlarged, transfer learning strategies will be investigated, and the system's integration into a comprehensive Parkinson's monitoring and evaluation platform will be looked into as future research areas. The results of this study could lead to a better quality of life for Parkinson's sufferers, individualized treatment, and an early classification.
Keywords: Parkinson's disease, tr Parkinson's disease, transfer learning models, deep learning, Hand drawing
Received: 23 Jun 2024; Accepted: 23 Aug 2024.
Copyright: © 2024 Farhah. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Nesren Farhah, Department of Health Informatics, College of Health Sciences, Saudi Electronic University, Dammam, Saudi Arabia
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