A Transfer Learning Approach for Clinical Detection Support of Monkeypox Skin Lesions
Monkeypox (MPX) is a disease caused by monkeypox virus (MPXV). It is a contagious disease and has associated symptoms of skin lesions, rashes, fever, and respiratory distress lymph swelling along with numerous neurological distresses. This can be a deadly disease, and the latest outbreak of it has s...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/13/8/1503 |
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| author | Maram Fahaad Almufareh Samabia Tehsin Mamoona Humayun Sumaira Kausar |
| author_facet | Maram Fahaad Almufareh Samabia Tehsin Mamoona Humayun Sumaira Kausar |
| author_sort | Maram Fahaad Almufareh |
| collection | DOAJ |
| description | Monkeypox (MPX) is a disease caused by monkeypox virus (MPXV). It is a contagious disease and has associated symptoms of skin lesions, rashes, fever, and respiratory distress lymph swelling along with numerous neurological distresses. This can be a deadly disease, and the latest outbreak of it has shown its spread to Europe, Australia, the United States, and Africa. Typically, diagnosis of MPX is performed through PCR, by taking a sample of the skin lesion. This procedure is risky for medical staff, as during sample collection, transmission and testing, they can be exposed to MPXV, and this infectious disease can be transferred to medical staff. In the current era, cutting-edge technologies such as IoT and artificial intelligence (AI) have made the diagnostics process smart and secure. IoT devices such as wearables and sensors permit seamless data collection while AI techniques utilize the data in disease diagnosis. Keeping in view the importance of these cutting-edge technologies, this paper presents a non-invasive, non-contact, computer-vision-based method for diagnosis of MPX by analyzing skin lesion images that are more smart and secure compared to traditional methods of diagnosis. The proposed methodology employs deep learning techniques to classify skin lesions as MPXV positive or not. Two datasets, the Kaggle Monkeypox Skin Lesion Dataset (MSLD) and the Monkeypox Skin Image Dataset (MSID), are used for evaluating the proposed methodology. The results on multiple deep learning models were evaluated using sensitivity, specificity and balanced accuracy. The proposed method has yielded highly promising results, demonstrating its potential for wide-scale deployment in detecting monkeypox. This smart and cost-effective solution can be effectively utilized in underprivileged areas where laboratory infrastructure may be lacking. |
| first_indexed | 2024-03-11T05:06:03Z |
| format | Article |
| id | doaj.art-652ebc34d8094da097607f2e034f9af7 |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-11T05:06:03Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-652ebc34d8094da097607f2e034f9af72023-11-17T18:55:58ZengMDPI AGDiagnostics2075-44182023-04-01138150310.3390/diagnostics13081503A Transfer Learning Approach for Clinical Detection Support of Monkeypox Skin LesionsMaram Fahaad Almufareh0Samabia Tehsin1Mamoona Humayun2Sumaira Kausar3Department of Information Systems, College of Computer and Information Sciences, Jouf University, Sakakah 72388, Saudi ArabiaDepartment of Computer Science, Bahria University, Islamabad 44220, PakistanDepartment of Information Systems, College of Computer and Information Sciences, Jouf University, Sakakah 72388, Saudi ArabiaDepartment of Computer Science, Bahria University, Islamabad 44220, PakistanMonkeypox (MPX) is a disease caused by monkeypox virus (MPXV). It is a contagious disease and has associated symptoms of skin lesions, rashes, fever, and respiratory distress lymph swelling along with numerous neurological distresses. This can be a deadly disease, and the latest outbreak of it has shown its spread to Europe, Australia, the United States, and Africa. Typically, diagnosis of MPX is performed through PCR, by taking a sample of the skin lesion. This procedure is risky for medical staff, as during sample collection, transmission and testing, they can be exposed to MPXV, and this infectious disease can be transferred to medical staff. In the current era, cutting-edge technologies such as IoT and artificial intelligence (AI) have made the diagnostics process smart and secure. IoT devices such as wearables and sensors permit seamless data collection while AI techniques utilize the data in disease diagnosis. Keeping in view the importance of these cutting-edge technologies, this paper presents a non-invasive, non-contact, computer-vision-based method for diagnosis of MPX by analyzing skin lesion images that are more smart and secure compared to traditional methods of diagnosis. The proposed methodology employs deep learning techniques to classify skin lesions as MPXV positive or not. Two datasets, the Kaggle Monkeypox Skin Lesion Dataset (MSLD) and the Monkeypox Skin Image Dataset (MSID), are used for evaluating the proposed methodology. The results on multiple deep learning models were evaluated using sensitivity, specificity and balanced accuracy. The proposed method has yielded highly promising results, demonstrating its potential for wide-scale deployment in detecting monkeypox. This smart and cost-effective solution can be effectively utilized in underprivileged areas where laboratory infrastructure may be lacking.https://www.mdpi.com/2075-4418/13/8/1503monkeypoxtransfer learningcomputer-aided diagnosisskin lesion detectionartificial intelligencedeep learning |
| spellingShingle | Maram Fahaad Almufareh Samabia Tehsin Mamoona Humayun Sumaira Kausar A Transfer Learning Approach for Clinical Detection Support of Monkeypox Skin Lesions Diagnostics monkeypox transfer learning computer-aided diagnosis skin lesion detection artificial intelligence deep learning |
| title | A Transfer Learning Approach for Clinical Detection Support of Monkeypox Skin Lesions |
| title_full | A Transfer Learning Approach for Clinical Detection Support of Monkeypox Skin Lesions |
| title_fullStr | A Transfer Learning Approach for Clinical Detection Support of Monkeypox Skin Lesions |
| title_full_unstemmed | A Transfer Learning Approach for Clinical Detection Support of Monkeypox Skin Lesions |
| title_short | A Transfer Learning Approach for Clinical Detection Support of Monkeypox Skin Lesions |
| title_sort | transfer learning approach for clinical detection support of monkeypox skin lesions |
| topic | monkeypox transfer learning computer-aided diagnosis skin lesion detection artificial intelligence deep learning |
| url | https://www.mdpi.com/2075-4418/13/8/1503 |
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