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Abstract: Early detection of skin cancer, including types such as basal cell carcinoma and melanoma, is crucial for effective treatment and reducing mortality rates. Skin cancer diagnoses often surpass those of all other cancers combined, with rising global mortality rates underscoring the need for improved detection methods. Early identification significantly enhances treatment outcomes, as confirmed by the World Health Organization. Advances in image processing and machine vision, particularly through convolutional neural networks (CNNs), have shown that deep learning models can outperform experienced dermatologists in skin cancer detection. In this project, we developed a deep learning model using Python libraries to categorize dermoscopic images from the MNIST HAM-10000 dataset into malignant or benign categories. The model's performance was further evaluated against three datasets from the International Skin Imaging Collaboration (ISIC) challenge archives, with the goal of enhancing early detection and treatment of skin cancer to lower mortality rates
Adhikari et al. (Thu,) studied this question.