SkinSight: advancing deep learning for skin cancer diagnosis and classification - Ashfaq, Nazish; Suhail, Zobia; Khalid, Adnan; Sarwar, Nadeem; Irshad, Asma; Yaman, Orhan; Almalki, Faris A. (Springer, 2025)
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.
Keywords: Skin cancer; Skin lesion analysis; Deep learning; multiclass skin cancer; Classification model; Fully convolutional neural networks