Deep learning based detection of monkeypox virus using skin lesion images
As we set into the second half of 2022, the world is still recovering from the two-year COVID-19 pandemic. However, over the past three months, the outbreak of the Monkeypox Virus (MPV) has led to fifty-two thousand confirmed cases and over one hundred deaths. This caused the World Health Organisati...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-06-01
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| Series: | Medicine in Novel Technology and Devices |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590093523000383 |
| _version_ | 1797790855078084608 |
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| author | Tushar Nayak Krishnaraj Chadaga Niranjana Sampathila Hilda Mayrose Nitila Gokulkrishnan Muralidhar Bairy G Srikanth Prabhu Swathi K. S Shashikiran Umakanth |
| author_facet | Tushar Nayak Krishnaraj Chadaga Niranjana Sampathila Hilda Mayrose Nitila Gokulkrishnan Muralidhar Bairy G Srikanth Prabhu Swathi K. S Shashikiran Umakanth |
| author_sort | Tushar Nayak |
| collection | DOAJ |
| description | As we set into the second half of 2022, the world is still recovering from the two-year COVID-19 pandemic. However, over the past three months, the outbreak of the Monkeypox Virus (MPV) has led to fifty-two thousand confirmed cases and over one hundred deaths. This caused the World Health Organisation to declare the outbreak a Public Health Emergency of International Concern (PHEIC). If this outbreak worsens, we could be looking at the Monkeypox virus causing the next global pandemic. As Monkeypox affects the human skin, the symptoms can be captured with regular imaging. Large samples of these images can be used as a training dataset for machine learning-based detection tools. Using a regular camera to capture the skin image of the infected person and running it against computer vision models is beneficial. In this research, we use deep learning to diagnose monkeypox from skin lesion images. Using a publicly available dataset, we tested the dataset on five pre-trained deep neural networks: GoogLeNet, Places365-GoogLeNet, SqueezeNet, AlexNet and ResNet-18. Hyperparameter was done to choose the best parameters. Performance metrics such as accuracy, precision, recall, f1-score and AUC were considered. Among the above models, ResNet18 was able to obtain the highest accuracy of 99.49%. The modified models obtained validation accuracies above 95%. The results prove that deep learning models such as the proposed model based on ResNet-18 can be deployed and can be crucial in battling the monkeypox virus. Since the used networks are optimized for efficiency, they can be used on performance limited devices such as smartphones with cameras. The addition of explainable artificial intelligence techniques LIME and GradCAM enables visual interpretation of the prediction made, helping health professionals using the model. |
| first_indexed | 2024-03-13T02:10:27Z |
| format | Article |
| id | doaj.art-c858f5bb09ab4add95820ed33c5bee40 |
| institution | Directory Open Access Journal |
| issn | 2590-0935 |
| language | English |
| last_indexed | 2024-03-13T02:10:27Z |
| publishDate | 2023-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Medicine in Novel Technology and Devices |
| spelling | doaj.art-c858f5bb09ab4add95820ed33c5bee402023-07-01T04:35:38ZengElsevierMedicine in Novel Technology and Devices2590-09352023-06-0118100243Deep learning based detection of monkeypox virus using skin lesion imagesTushar Nayak0Krishnaraj Chadaga1Niranjana Sampathila2Hilda Mayrose3Nitila Gokulkrishnan4Muralidhar Bairy G5Srikanth Prabhu6Swathi K. S7Shashikiran Umakanth8Department of Biomedical Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, IndiaDepartment of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, IndiaDepartment of Biomedical Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India; Corresponding author.Department of Biomedical Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, IndiaDepartment of Biomedical Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, IndiaDepartment of Biomedical Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, IndiaDepartment of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, IndiaPrasanna School of Public Health, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, IndiaDepartment of Medicine, Dr. T.M.A. Pai Hospital, Manipal Academy of Higher Education, Udupi, Karnataka, 576101, IndiaAs we set into the second half of 2022, the world is still recovering from the two-year COVID-19 pandemic. However, over the past three months, the outbreak of the Monkeypox Virus (MPV) has led to fifty-two thousand confirmed cases and over one hundred deaths. This caused the World Health Organisation to declare the outbreak a Public Health Emergency of International Concern (PHEIC). If this outbreak worsens, we could be looking at the Monkeypox virus causing the next global pandemic. As Monkeypox affects the human skin, the symptoms can be captured with regular imaging. Large samples of these images can be used as a training dataset for machine learning-based detection tools. Using a regular camera to capture the skin image of the infected person and running it against computer vision models is beneficial. In this research, we use deep learning to diagnose monkeypox from skin lesion images. Using a publicly available dataset, we tested the dataset on five pre-trained deep neural networks: GoogLeNet, Places365-GoogLeNet, SqueezeNet, AlexNet and ResNet-18. Hyperparameter was done to choose the best parameters. Performance metrics such as accuracy, precision, recall, f1-score and AUC were considered. Among the above models, ResNet18 was able to obtain the highest accuracy of 99.49%. The modified models obtained validation accuracies above 95%. The results prove that deep learning models such as the proposed model based on ResNet-18 can be deployed and can be crucial in battling the monkeypox virus. Since the used networks are optimized for efficiency, they can be used on performance limited devices such as smartphones with cameras. The addition of explainable artificial intelligence techniques LIME and GradCAM enables visual interpretation of the prediction made, helping health professionals using the model.http://www.sciencedirect.com/science/article/pii/S2590093523000383Deep learningDisease diagnosisImage processingMonkeypox virusMachine learningTransfer learning |
| spellingShingle | Tushar Nayak Krishnaraj Chadaga Niranjana Sampathila Hilda Mayrose Nitila Gokulkrishnan Muralidhar Bairy G Srikanth Prabhu Swathi K. S Shashikiran Umakanth Deep learning based detection of monkeypox virus using skin lesion images Medicine in Novel Technology and Devices Deep learning Disease diagnosis Image processing Monkeypox virus Machine learning Transfer learning |
| title | Deep learning based detection of monkeypox virus using skin lesion images |
| title_full | Deep learning based detection of monkeypox virus using skin lesion images |
| title_fullStr | Deep learning based detection of monkeypox virus using skin lesion images |
| title_full_unstemmed | Deep learning based detection of monkeypox virus using skin lesion images |
| title_short | Deep learning based detection of monkeypox virus using skin lesion images |
| title_sort | deep learning based detection of monkeypox virus using skin lesion images |
| topic | Deep learning Disease diagnosis Image processing Monkeypox virus Machine learning Transfer learning |
| url | http://www.sciencedirect.com/science/article/pii/S2590093523000383 |
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