Deep Transfer Learning Based Parkinson’s Disease Detection Using Optimized Feature Selection
Parkinson’s disease (PD) is one of the chronic neurological diseases whose progression is slow and symptoms have similarities with other diseases. Early detection and diagnosis of PD is crucial to prescribe proper treatment for patient’s productive and healthy lives. The diseas...
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IEEE
2023-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10005184/ |
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| author | Sura Mahmood Abdullah Thekra Abbas Munzir Hubiba Bashir Ishfaq Ahmad Khaja Musheer Ahmad Naglaa F. Soliman Walid El-Shafai |
| author_facet | Sura Mahmood Abdullah Thekra Abbas Munzir Hubiba Bashir Ishfaq Ahmad Khaja Musheer Ahmad Naglaa F. Soliman Walid El-Shafai |
| author_sort | Sura Mahmood Abdullah |
| collection | DOAJ |
| description | Parkinson’s disease (PD) is one of the chronic neurological diseases whose progression is slow and symptoms have similarities with other diseases. Early detection and diagnosis of PD is crucial to prescribe proper treatment for patient’s productive and healthy lives. The disease’s symptoms are characterized by tremors, muscle rigidity, slowness in movements, balancing along with other psychiatric symptoms. The dynamics of handwritten records served as one of the dominant mechanisms which support PD detection and assessment. Several machine learning methods have been investigated for the early detection of this disease. But most of these handcrafted feature extraction techniques predominantly suffer from low performance accuracy issues. This cannot be tolerable for dealing with detection of such a chronic ailment. To this end, an efficient deep learning model is proposed which can assist to have early detection of Parkinson’s disease. The significant contribution of the proposed model is to select the most optimum features which have the effect of getting the high-performance accuracies. The feature optimization is done through genetic algorithm wherein <inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula>-Nearest Neighbour technique. The proposed novel model results into detection accuracy higher than 95%, precision of 98%, area under curve of 0.90 with a loss of 0.12 only. The performance of proposed model is compared with some state-of-the-art machine learning and deep learning-based PD detection approaches to demonstrate the better detection ability of our model. |
| format | Article |
| id | doaj-art-64c47c930bef4ad89c6a532d2ab6c7d6 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-64c47c930bef4ad89c6a532d2ab6c7d62024-12-11T00:04:06ZengIEEEIEEE Access2169-35362023-01-01113511352410.1109/ACCESS.2023.323396910005184Deep Transfer Learning Based Parkinson’s Disease Detection Using Optimized Feature SelectionSura Mahmood Abdullah0Thekra Abbas1Munzir Hubiba Bashir2Ishfaq Ahmad Khaja3https://orcid.org/0000-0001-9173-3952Musheer Ahmad4https://orcid.org/0000-0002-4915-9325Naglaa F. Soliman5https://orcid.org/0000-0001-7322-1857Walid El-Shafai6https://orcid.org/0000-0001-7509-2120Department of Computer Sciences, University of Technology, Baghdad, IraqDepartment of Computer Sciences, College of Science, Mustansiriyah University, Baghdad, IraqDepartment of Computer Engineering, Jamia Millia Islamia, New Delhi, IndiaDepartment of Computer Engineering, Jamia Millia Islamia, New Delhi, IndiaDepartment of Computer Engineering, Jamia Millia Islamia, New Delhi, IndiaDepartment of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi ArabiaComputer Science Department, Security Engineering Laboratory, Prince Sultan University, Riyadh, Saudi ArabiaParkinson’s disease (PD) is one of the chronic neurological diseases whose progression is slow and symptoms have similarities with other diseases. Early detection and diagnosis of PD is crucial to prescribe proper treatment for patient’s productive and healthy lives. The disease’s symptoms are characterized by tremors, muscle rigidity, slowness in movements, balancing along with other psychiatric symptoms. The dynamics of handwritten records served as one of the dominant mechanisms which support PD detection and assessment. Several machine learning methods have been investigated for the early detection of this disease. But most of these handcrafted feature extraction techniques predominantly suffer from low performance accuracy issues. This cannot be tolerable for dealing with detection of such a chronic ailment. To this end, an efficient deep learning model is proposed which can assist to have early detection of Parkinson’s disease. The significant contribution of the proposed model is to select the most optimum features which have the effect of getting the high-performance accuracies. The feature optimization is done through genetic algorithm wherein <inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula>-Nearest Neighbour technique. The proposed novel model results into detection accuracy higher than 95%, precision of 98%, area under curve of 0.90 with a loss of 0.12 only. The performance of proposed model is compared with some state-of-the-art machine learning and deep learning-based PD detection approaches to demonstrate the better detection ability of our model.https://ieeexplore.ieee.org/document/10005184/Parkinson’s diseaseneurological disorderhandwritten recordstransfer learningdeep learning |
| spellingShingle | Sura Mahmood Abdullah Thekra Abbas Munzir Hubiba Bashir Ishfaq Ahmad Khaja Musheer Ahmad Naglaa F. Soliman Walid El-Shafai Deep Transfer Learning Based Parkinson’s Disease Detection Using Optimized Feature Selection IEEE Access Parkinson’s disease neurological disorder handwritten records transfer learning deep learning |
| title | Deep Transfer Learning Based Parkinson’s Disease Detection Using Optimized Feature Selection |
| title_full | Deep Transfer Learning Based Parkinson’s Disease Detection Using Optimized Feature Selection |
| title_fullStr | Deep Transfer Learning Based Parkinson’s Disease Detection Using Optimized Feature Selection |
| title_full_unstemmed | Deep Transfer Learning Based Parkinson’s Disease Detection Using Optimized Feature Selection |
| title_short | Deep Transfer Learning Based Parkinson’s Disease Detection Using Optimized Feature Selection |
| title_sort | deep transfer learning based parkinson x2019 s disease detection using optimized feature selection |
| topic | Parkinson’s disease neurological disorder handwritten records transfer learning deep learning |
| url | https://ieeexplore.ieee.org/document/10005184/ |
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