A fuzzy rank-based deep ensemble methodology for multi-class skin cancer classification
Abstract Skin cancer is widespread and can be potentially fatal. According to the World Health Organisation (WHO), it has been identified as a leading cause of mortality. It is essential to detect skin cancer early so that effective treatment can be provided at an initial stage. In this study, the w...
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Nature Portfolio
2025-02-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-90423-3 |
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| author | Arindam Halder Anogh Dalal Sanghita Gharami Marcin Wozniak Muhammad Fazal Ijaz Pawan Kumar Singh |
| author_facet | Arindam Halder Anogh Dalal Sanghita Gharami Marcin Wozniak Muhammad Fazal Ijaz Pawan Kumar Singh |
| author_sort | Arindam Halder |
| collection | DOAJ |
| description | Abstract Skin cancer is widespread and can be potentially fatal. According to the World Health Organisation (WHO), it has been identified as a leading cause of mortality. It is essential to detect skin cancer early so that effective treatment can be provided at an initial stage. In this study, the widely-used HAM10000 dataset, containing high-resolution images of various skin lesions, is employed to train and evaluate. Our methodology for the HAM10000 dataset involves balancing the imbalanced dataset by augmenting images followed by splitting the dataset into train, test and validation set, preprocessing the images, training the individual models Xception, InceptionResNetV2 and MobileNetV2, and then combining their outputs using fuzzy logic to generate a final prediction. We examined the performance of the ensemble using standard metrics like classification accuracy, confusion matrix, etc. and achieved an impressive accuracy of 95.14% and the result demonstrates the effectiveness of our approach in accurately identifying skin cancer lesions. To further assess the efficiency of the model, additional tests have been performed on the DermaMNIST dataset from the MedMNISTv2 collection. The model performs well on the dataset and transcends the benchmark accuracy of 76.8%, achieving 78.25%. Thus the model is efficient for skin cancer classification, showcasing its potential for clinical applications. |
| format | Article |
| id | doaj-art-eb6b69a995ca4a7b9c2f35e65840d366 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-eb6b69a995ca4a7b9c2f35e65840d3662025-08-20T03:11:07ZengNature PortfolioScientific Reports2045-23222025-02-0115112510.1038/s41598-025-90423-3A fuzzy rank-based deep ensemble methodology for multi-class skin cancer classificationArindam Halder0Anogh Dalal1Sanghita Gharami2Marcin Wozniak3Muhammad Fazal Ijaz4Pawan Kumar Singh5Department of Information Technology, Jadavpur UniversityDepartment of Information Technology, Jadavpur UniversityDepartment of Information Technology, Jadavpur UniversityFaculty of Applied Mathematics, Silesian University of TechnologySchool of IT and Engineering, Melbourne Institute of TechnologyDepartment of Information Technology, Jadavpur UniversityAbstract Skin cancer is widespread and can be potentially fatal. According to the World Health Organisation (WHO), it has been identified as a leading cause of mortality. It is essential to detect skin cancer early so that effective treatment can be provided at an initial stage. In this study, the widely-used HAM10000 dataset, containing high-resolution images of various skin lesions, is employed to train and evaluate. Our methodology for the HAM10000 dataset involves balancing the imbalanced dataset by augmenting images followed by splitting the dataset into train, test and validation set, preprocessing the images, training the individual models Xception, InceptionResNetV2 and MobileNetV2, and then combining their outputs using fuzzy logic to generate a final prediction. We examined the performance of the ensemble using standard metrics like classification accuracy, confusion matrix, etc. and achieved an impressive accuracy of 95.14% and the result demonstrates the effectiveness of our approach in accurately identifying skin cancer lesions. To further assess the efficiency of the model, additional tests have been performed on the DermaMNIST dataset from the MedMNISTv2 collection. The model performs well on the dataset and transcends the benchmark accuracy of 76.8%, achieving 78.25%. Thus the model is efficient for skin cancer classification, showcasing its potential for clinical applications.https://doi.org/10.1038/s41598-025-90423-3Skin Cancer image classificationFuzzy ensembleDeep learningHAM10000DermaMNISTXception |
| spellingShingle | Arindam Halder Anogh Dalal Sanghita Gharami Marcin Wozniak Muhammad Fazal Ijaz Pawan Kumar Singh A fuzzy rank-based deep ensemble methodology for multi-class skin cancer classification Scientific Reports Skin Cancer image classification Fuzzy ensemble Deep learning HAM10000 DermaMNIST Xception |
| title | A fuzzy rank-based deep ensemble methodology for multi-class skin cancer classification |
| title_full | A fuzzy rank-based deep ensemble methodology for multi-class skin cancer classification |
| title_fullStr | A fuzzy rank-based deep ensemble methodology for multi-class skin cancer classification |
| title_full_unstemmed | A fuzzy rank-based deep ensemble methodology for multi-class skin cancer classification |
| title_short | A fuzzy rank-based deep ensemble methodology for multi-class skin cancer classification |
| title_sort | fuzzy rank based deep ensemble methodology for multi class skin cancer classification |
| topic | Skin Cancer image classification Fuzzy ensemble Deep learning HAM10000 DermaMNIST Xception |
| url | https://doi.org/10.1038/s41598-025-90423-3 |
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