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<p>Skin cancer is common but still poses significant dangers. Early detection is crucial. Recognizing the warning signs is important. Spotting these signs early can improve your prognosis and lead to more effective treatment. Melanoma is one of the most common and potentially deadly types of cancer in the world. If not discovered quickly, this type of skin cancer can spread to other parts of the body. Automated diagnostic technologies have greatly advanced medicine. They help the public and medical professionals recognize particular disorders. To further this progress, we offer a hybrid method for melanoma skin cancer detection. Our approach enables examination of any skin lesion that appears worrisome. We use machine learning classifiers and ensemble learning techniques. Our work presents a hybrid method for melanoma skin cancer detection. The proposed model was trained on characteristics describing lesion boundaries, texture, and colour. It achieves a recall of 1, a precision of 0.8667, an accuracy of 0.9524, and an F1 score of 0.9286. These outcomes demonstrate the model’s accuracy and resilience in detecting positive cases while reducing false positives. The effectiveness of our approach suggests promise for automated diagnostic systems across various ailments, highlighting the potential for prompt identification and better medical outcomes. Our work contributes to SDG 3: Good Health and Well-being by advancing early and accurate detection of melanoma skin cancer through the proposed model.</p>

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Unveiling skin disease patterns: leveraging explainable artificial intelligence for enhanced classification via ensemble learning

  • 期刊论文

  • 发表日期：

    2025年11月10日

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  • Ashok Kumar,
  • Pankaj Kumar,
  • Shalya Saxena,
  • Ashish Srivastava,
  • Vipul Narayan,
  • Swapnita Srivastava

摘要

Skin cancer is common but still poses significant dangers. Early detection is crucial. Recognizing the warning signs is important. Spotting these signs early can improve your prognosis and lead to more effective treatment. Melanoma is one of the most common and potentially deadly types of cancer in the world. If not discovered quickly, this type of skin cancer can spread to other parts of the body. Automated diagnostic technologies have greatly advanced medicine. They help the public and medical professionals recognize particular disorders. To further this progress, we offer a hybrid method for melanoma skin cancer detection. Our approach enables examination of any skin lesion that appears worrisome. We use machine learning classifiers and ensemble learning techniques. Our work presents a hybrid method for melanoma skin cancer detection. The proposed model was trained on characteristics describing lesion boundaries, texture, and colour. It achieves a recall of 1, a precision of 0.8667, an accuracy of 0.9524, and an F1 score of 0.9286. These outcomes demonstrate the model’s accuracy and resilience in detecting positive cases while reducing false positives. The effectiveness of our approach suggests promise for automated diagnostic systems across various ailments, highlighting the potential for prompt identification and better medical outcomes. Our work contributes to SDG 3: Good Health and Well-being by advancing early and accurate detection of melanoma skin cancer through the proposed model.