Optimized MobileNetV3: a deep learning-based Parkinson’s disease classification using fused images
Background and Objective Parkinson’s disease (PD) is a progressive neurological condition that manifests motor and non-motor symptoms. Early in the course of the disease, PD patients frequently experience vocal difficulties. In the beginning, preprocessing procedures were used with multi-focus image...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
PeerJ Inc.
2023-11-01
|
| Series: | PeerJ Computer Science |
| Subjects: | |
| Online Access: | https://peerj.com/articles/cs-1702.pdf |
| _version_ | 1827630694267879424 |
|---|---|
| author | Sukanya Pechetti Battula Srinivasa Rao |
| author_facet | Sukanya Pechetti Battula Srinivasa Rao |
| author_sort | Sukanya Pechetti |
| collection | DOAJ |
| description | Background and Objective Parkinson’s disease (PD) is a progressive neurological condition that manifests motor and non-motor symptoms. Early in the course of the disease, PD patients frequently experience vocal difficulties. In the beginning, preprocessing procedures were used with multi-focus image fusion to enhance the quality of input images. It is essential to diagnose and treat PD early to ensure that patients live healthy and productive lives. Methods Tremors, rigidity in the muscles, slow movement, difficulty balance, and other psychological symptoms are some of the disease’s symptoms. One of the critical mechanisms supporting PD identification and assessment is the dynamics of handwritten records. Several machine-learning techniques have been researched for the early detection of this disease. Yet the main problem with most of these manual feature extraction methods is their poor performance and accuracy. Results This cannot be acceptable when discovering such a chronic condition. For this purpose, a powerful deep learning model is suggested to help with the early diagnosis of Parkinson’s disease. Therefore, we proposed MobileNetV3-based classification. To enhance the classification performances even more, the MobileNetV3-based approach was optimized by the Improved Dwarf Mongoose Optimization algorithm (IDMO). Conclusion The Pyramid channel-based feature attention network (PCFAN) chooses the critical features. The efficiency of the approaches is tested using the PPMI and NTUA datasets. Our proposed approach obtains 99.34% accuracy, 98.53% sensitivity, 97.78% specificity, and 99.12% F-score compared to previous methods. |
| first_indexed | 2024-03-09T14:10:24Z |
| format | Article |
| id | doaj.art-f2a821f23a864c9d99f6c7b62e0ccb6d |
| institution | Directory Open Access Journal |
| issn | 2376-5992 |
| language | English |
| last_indexed | 2024-03-09T14:10:24Z |
| publishDate | 2023-11-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj.art-f2a821f23a864c9d99f6c7b62e0ccb6d2023-11-29T15:05:16ZengPeerJ Inc.PeerJ Computer Science2376-59922023-11-019e170210.7717/peerj-cs.1702Optimized MobileNetV3: a deep learning-based Parkinson’s disease classification using fused imagesSukanya PechettiBattula Srinivasa RaoBackground and Objective Parkinson’s disease (PD) is a progressive neurological condition that manifests motor and non-motor symptoms. Early in the course of the disease, PD patients frequently experience vocal difficulties. In the beginning, preprocessing procedures were used with multi-focus image fusion to enhance the quality of input images. It is essential to diagnose and treat PD early to ensure that patients live healthy and productive lives. Methods Tremors, rigidity in the muscles, slow movement, difficulty balance, and other psychological symptoms are some of the disease’s symptoms. One of the critical mechanisms supporting PD identification and assessment is the dynamics of handwritten records. Several machine-learning techniques have been researched for the early detection of this disease. Yet the main problem with most of these manual feature extraction methods is their poor performance and accuracy. Results This cannot be acceptable when discovering such a chronic condition. For this purpose, a powerful deep learning model is suggested to help with the early diagnosis of Parkinson’s disease. Therefore, we proposed MobileNetV3-based classification. To enhance the classification performances even more, the MobileNetV3-based approach was optimized by the Improved Dwarf Mongoose Optimization algorithm (IDMO). Conclusion The Pyramid channel-based feature attention network (PCFAN) chooses the critical features. The efficiency of the approaches is tested using the PPMI and NTUA datasets. Our proposed approach obtains 99.34% accuracy, 98.53% sensitivity, 97.78% specificity, and 99.12% F-score compared to previous methods.https://peerj.com/articles/cs-1702.pdfParkinson’s diseaseFeature extractionMobileNetV3Pyramid channel-based feature attention networkImproved Dwarf Mongoose Optimization algorithm |
| spellingShingle | Sukanya Pechetti Battula Srinivasa Rao Optimized MobileNetV3: a deep learning-based Parkinson’s disease classification using fused images PeerJ Computer Science Parkinson’s disease Feature extraction MobileNetV3 Pyramid channel-based feature attention network Improved Dwarf Mongoose Optimization algorithm |
| title | Optimized MobileNetV3: a deep learning-based Parkinson’s disease classification using fused images |
| title_full | Optimized MobileNetV3: a deep learning-based Parkinson’s disease classification using fused images |
| title_fullStr | Optimized MobileNetV3: a deep learning-based Parkinson’s disease classification using fused images |
| title_full_unstemmed | Optimized MobileNetV3: a deep learning-based Parkinson’s disease classification using fused images |
| title_short | Optimized MobileNetV3: a deep learning-based Parkinson’s disease classification using fused images |
| title_sort | optimized mobilenetv3 a deep learning based parkinson s disease classification using fused images |
| topic | Parkinson’s disease Feature extraction MobileNetV3 Pyramid channel-based feature attention network Improved Dwarf Mongoose Optimization algorithm |
| url | https://peerj.com/articles/cs-1702.pdf |
| work_keys_str_mv | AT sukanyapechetti optimizedmobilenetv3adeeplearningbasedparkinsonsdiseaseclassificationusingfusedimages AT battulasrinivasarao optimizedmobilenetv3adeeplearningbasedparkinsonsdiseaseclassificationusingfusedimages |