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<p>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.</p>

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A fuzzy rank-based deep ensemble methodology for multi-class skin cancer classification

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年2月20日

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  • Arindam Halder,
  • Anogh Dalal,
  • Sanghita Gharami,
  • Marcin Wozniak,
  • Muhammad Fazal Ijaz,
  • Pawan Kumar Singh

摘要

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.