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<p>Skin cancer is most likely to disseminate to other parts of the human body if it is not detected and treated in a timely manner. Consequently, early detection is crucial for prompt and effective treatment. The evident similarity between skin conditions has complicated medical diagnosis. Although melanoma is the most well-known form of skin cancer, other diseases have caused a significant number of deaths in recent years. Recent advances in computerized methods for mak- ing these diagnoses have made them more accurate and quicker, which is very encouraging. The absence of sizable datasets is one of the greatest obstacles to developing a reliable automatic classification system. For this purpose, the ISIC Skin Lesion Classification Challenge provided 25331 images from eight distinct classifications. This paper presents a CNN-based deep learning model for cuta- neous cancer detection. Its primary objective is to categorize skin lesions based on Dermoscope images. With a sensitivity of 55.32%, a specificity of 88.92%, an accuracy of 90.18%, a precision of 91.01%, a dice accuracy of 90.80%, and a jac- card accuracy of 83.37%, our method has yielded results that are significantly superior to those of existing methods. The proposed technique is significantly superior to the existing methods for recognizing and categorizing skin diseases.</p>

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SkinSight: advancing deep learning for skin cancer diagnosis and classification

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年5月4日

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  • Nazish Ashfaq,
  • Zobia Suhail,
  • Adnan Khalid,
  • Nadeem Sarwar,
  • Asma Irshad,
  • Orhan Yaman,
  • Abeer Alubaidi,
  • Fatma M. Ahmed,
  • Faris A. Almalki

摘要

Skin cancer is most likely to disseminate to other parts of the human body if it is not detected and treated in a timely manner. Consequently, early detection is crucial for prompt and effective treatment. The evident similarity between skin conditions has complicated medical diagnosis. Although melanoma is the most well-known form of skin cancer, other diseases have caused a significant number of deaths in recent years. Recent advances in computerized methods for mak- ing these diagnoses have made them more accurate and quicker, which is very encouraging. The absence of sizable datasets is one of the greatest obstacles to developing a reliable automatic classification system. For this purpose, the ISIC Skin Lesion Classification Challenge provided 25331 images from eight distinct classifications. This paper presents a CNN-based deep learning model for cuta- neous cancer detection. Its primary objective is to categorize skin lesions based on Dermoscope images. With a sensitivity of 55.32%, a specificity of 88.92%, an accuracy of 90.18%, a precision of 91.01%, a dice accuracy of 90.80%, and a jac- card accuracy of 83.37%, our method has yielded results that are significantly superior to those of existing methods. The proposed technique is significantly superior to the existing methods for recognizing and categorizing skin diseases.