Intelligent skin disease prediction system using transfer learning and explainable artificial intelligence
Abstract Skin diseases impact millions of people around the world and pose a severe risk to public health. These diseases have a wide range of effects on the skin’s structure, functionality, and appearance. Identifying and predicting skin diseases are laborious processes that require a complete phys...
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| Format: | Article |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-83966-4 |
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| author | Sagheer Abbas Fahad Ahmed Wasim Ahmad Khan Munir Ahmad Muhammad Adnan Khan Taher M. Ghazal |
| author_facet | Sagheer Abbas Fahad Ahmed Wasim Ahmad Khan Munir Ahmad Muhammad Adnan Khan Taher M. Ghazal |
| author_sort | Sagheer Abbas |
| collection | DOAJ |
| description | Abstract Skin diseases impact millions of people around the world and pose a severe risk to public health. These diseases have a wide range of effects on the skin’s structure, functionality, and appearance. Identifying and predicting skin diseases are laborious processes that require a complete physical examination, a review of the patient’s medical history, and proper laboratory diagnostic testing. Additionally, it necessitates a significant number of histological and clinical characteristics for examination and subsequent treatment. As a disease’s complexity and quantity of features grow, identifying and predicting it becomes more challenging. This research proposes a deep learning (DL) model utilizing transfer learning (TL) to quickly identify skin diseases like chickenpox, measles, and monkeypox. A pre-trained VGG16 is used for transfer learning. The VGG16 can identify and predict diseases more quickly by learning symptom patterns. Images of the skin from the four classes of chickenpox, measles, monkeypox, and normal are included in the dataset. The dataset is separated into training and testing. The experimental results performed on the dataset demonstrate that the VGG16 model can identify and predict skin diseases with 93.29% testing accuracy. However, the VGG16 model does not explain why and how the system operates because deep learning models are black boxes. Deep learning models’ opacity stands in the way of their widespread application in the healthcare sector. In order to make this a valuable system for the health sector, this article employs layer-wise relevance propagation (LRP) to determine the relevance scores of each input. The identified symptoms provide valuable insights that could support timely diagnosis and treatment decisions for skin diseases. |
| format | Article |
| id | doaj-art-c7b3c0d11e464126a9ff00ae6d9a626b |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-c7b3c0d11e464126a9ff00ae6d9a626b2025-01-12T12:24:27ZengNature PortfolioScientific Reports2045-23222025-01-0115111310.1038/s41598-024-83966-4Intelligent skin disease prediction system using transfer learning and explainable artificial intelligenceSagheer Abbas0Fahad Ahmed1Wasim Ahmad Khan2Munir Ahmad3Muhammad Adnan Khan4Taher M. Ghazal5Department of Computer Science, Prince Mohammad Bin Fahd UniversitySchool of Computer Science, National College of Business Administration and EconomicsDepartment of Computer Science, Baba Guru Nanak UniversitySchool of Computer Science, National College of Business Administration and EconomicsDepartment of Software, Faculty of Artificial Intelligence and Software, Gachon UniversityResearch Innovation and Entrepreneurship Unit, University of BuraimiAbstract Skin diseases impact millions of people around the world and pose a severe risk to public health. These diseases have a wide range of effects on the skin’s structure, functionality, and appearance. Identifying and predicting skin diseases are laborious processes that require a complete physical examination, a review of the patient’s medical history, and proper laboratory diagnostic testing. Additionally, it necessitates a significant number of histological and clinical characteristics for examination and subsequent treatment. As a disease’s complexity and quantity of features grow, identifying and predicting it becomes more challenging. This research proposes a deep learning (DL) model utilizing transfer learning (TL) to quickly identify skin diseases like chickenpox, measles, and monkeypox. A pre-trained VGG16 is used for transfer learning. The VGG16 can identify and predict diseases more quickly by learning symptom patterns. Images of the skin from the four classes of chickenpox, measles, monkeypox, and normal are included in the dataset. The dataset is separated into training and testing. The experimental results performed on the dataset demonstrate that the VGG16 model can identify and predict skin diseases with 93.29% testing accuracy. However, the VGG16 model does not explain why and how the system operates because deep learning models are black boxes. Deep learning models’ opacity stands in the way of their widespread application in the healthcare sector. In order to make this a valuable system for the health sector, this article employs layer-wise relevance propagation (LRP) to determine the relevance scores of each input. The identified symptoms provide valuable insights that could support timely diagnosis and treatment decisions for skin diseases.https://doi.org/10.1038/s41598-024-83966-4Artificial intelligence (AI)Machine learning (ML)Deep learning (DL)Transfer learning (TL)VGG16Chickenpox |
| spellingShingle | Sagheer Abbas Fahad Ahmed Wasim Ahmad Khan Munir Ahmad Muhammad Adnan Khan Taher M. Ghazal Intelligent skin disease prediction system using transfer learning and explainable artificial intelligence Scientific Reports Artificial intelligence (AI) Machine learning (ML) Deep learning (DL) Transfer learning (TL) VGG16 Chickenpox |
| title | Intelligent skin disease prediction system using transfer learning and explainable artificial intelligence |
| title_full | Intelligent skin disease prediction system using transfer learning and explainable artificial intelligence |
| title_fullStr | Intelligent skin disease prediction system using transfer learning and explainable artificial intelligence |
| title_full_unstemmed | Intelligent skin disease prediction system using transfer learning and explainable artificial intelligence |
| title_short | Intelligent skin disease prediction system using transfer learning and explainable artificial intelligence |
| title_sort | intelligent skin disease prediction system using transfer learning and explainable artificial intelligence |
| topic | Artificial intelligence (AI) Machine learning (ML) Deep learning (DL) Transfer learning (TL) VGG16 Chickenpox |
| url | https://doi.org/10.1038/s41598-024-83966-4 |
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