Detection of Monkeypox from skin lesion images using deep learning networks and explainable artificial intelligence
Monkeypox (Mpox) resurfaced in January 2022 as a rare zoonotic disease that spreads to many countries. Though the virus is not as dangerous as COVID-19, it has still caused many fatalities worldwide. The Mpox virus spreads when people are in close contact with infected individuals. Among many sympto...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2023-12-01
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| Series: | Applied Mathematics in Science and Engineering |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/27690911.2023.2225698 |
| _version_ | 1797640989272178688 |
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| author | Tushar Nayak Krishnaraj Chadaga Niranjana Sampathila Hilda Mayrose G. Muralidhar Bairy Srikanth Prabhu Swathi S. Katta Shashikiran Umakanth |
| author_facet | Tushar Nayak Krishnaraj Chadaga Niranjana Sampathila Hilda Mayrose G. Muralidhar Bairy Srikanth Prabhu Swathi S. Katta Shashikiran Umakanth |
| author_sort | Tushar Nayak |
| collection | DOAJ |
| description | Monkeypox (Mpox) resurfaced in January 2022 as a rare zoonotic disease that spreads to many countries. Though the virus is not as dangerous as COVID-19, it has still caused many fatalities worldwide. The Mpox virus spreads when people are in close contact with infected individuals. Among many symptoms, the disease also causes skin rashes, and medical imaging can be used to diagnose the virus successfully. However, other diseases such as smallpox, chickenpox, and measles also cause similar skin rashes. Hence, artificial intelligence (AI) and machine learning (ML) can be highly beneficial in diagnosing Mpox from other similar diseases. After extensive model training, it is advantageous to use a standard camera to capture skin images of an infected patient and run it against deep learning (DL) models. In this research, we have used transfer learning models such as residual networks and SqueezeNet to diagnose Mpox from measles, chickenpox and healthy patients. An average accuracy of 91.19% and an F1-score of 92.55% were obtained for the Mpox class. The findings show that the models can be useful in detecting the contagious virus. Since the classifiers are easily deployable, they can be used on camera-ready devices such as phones and laptops. |
| first_indexed | 2024-03-11T13:39:06Z |
| format | Article |
| id | doaj.art-21467ea9720c4642909e368647883ce1 |
| institution | Directory Open Access Journal |
| issn | 2769-0911 |
| language | English |
| last_indexed | 2024-03-11T13:39:06Z |
| publishDate | 2023-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Applied Mathematics in Science and Engineering |
| spelling | doaj.art-21467ea9720c4642909e368647883ce12023-11-02T13:48:32ZengTaylor & Francis GroupApplied Mathematics in Science and Engineering2769-09112023-12-0131110.1080/27690911.2023.22256982225698Detection of Monkeypox from skin lesion images using deep learning networks and explainable artificial intelligenceTushar Nayak0Krishnaraj Chadaga1Niranjana Sampathila2Hilda Mayrose3G. Muralidhar Bairy4Srikanth Prabhu5Swathi S. Katta6Shashikiran Umakanth7Manipal Academy of Higher EducationManipal Academy of Higher EducationManipal Academy of Higher EducationManipal Academy of Higher EducationManipal Academy of Higher EducationManipal Academy of Higher EducationManipal Academy of Higher EducationDr. T.M.A. Pai Hospital, Manipal Academy of Higher EducationMonkeypox (Mpox) resurfaced in January 2022 as a rare zoonotic disease that spreads to many countries. Though the virus is not as dangerous as COVID-19, it has still caused many fatalities worldwide. The Mpox virus spreads when people are in close contact with infected individuals. Among many symptoms, the disease also causes skin rashes, and medical imaging can be used to diagnose the virus successfully. However, other diseases such as smallpox, chickenpox, and measles also cause similar skin rashes. Hence, artificial intelligence (AI) and machine learning (ML) can be highly beneficial in diagnosing Mpox from other similar diseases. After extensive model training, it is advantageous to use a standard camera to capture skin images of an infected patient and run it against deep learning (DL) models. In this research, we have used transfer learning models such as residual networks and SqueezeNet to diagnose Mpox from measles, chickenpox and healthy patients. An average accuracy of 91.19% and an F1-score of 92.55% were obtained for the Mpox class. The findings show that the models can be useful in detecting the contagious virus. Since the classifiers are easily deployable, they can be used on camera-ready devices such as phones and laptops.http://dx.doi.org/10.1080/27690911.2023.2225698monkeypoxchickenpoxmeaslesskin lesiondeep learningexplainable artificial intelligence (xai) |
| spellingShingle | Tushar Nayak Krishnaraj Chadaga Niranjana Sampathila Hilda Mayrose G. Muralidhar Bairy Srikanth Prabhu Swathi S. Katta Shashikiran Umakanth Detection of Monkeypox from skin lesion images using deep learning networks and explainable artificial intelligence Applied Mathematics in Science and Engineering monkeypox chickenpox measles skin lesion deep learning explainable artificial intelligence (xai) |
| title | Detection of Monkeypox from skin lesion images using deep learning networks and explainable artificial intelligence |
| title_full | Detection of Monkeypox from skin lesion images using deep learning networks and explainable artificial intelligence |
| title_fullStr | Detection of Monkeypox from skin lesion images using deep learning networks and explainable artificial intelligence |
| title_full_unstemmed | Detection of Monkeypox from skin lesion images using deep learning networks and explainable artificial intelligence |
| title_short | Detection of Monkeypox from skin lesion images using deep learning networks and explainable artificial intelligence |
| title_sort | detection of monkeypox from skin lesion images using deep learning networks and explainable artificial intelligence |
| topic | monkeypox chickenpox measles skin lesion deep learning explainable artificial intelligence (xai) |
| url | http://dx.doi.org/10.1080/27690911.2023.2225698 |
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