Abstract
Abstract: Skin cancer disease is recognized as one of the most perilous types of cancer & an increasing rate of mortality is attributed to insufficient awareness of symptoms and preventive measures. Consequently, it is of very importance to detect skincancer at the beginning of stage to prevent its spread. Various types of skin cancer exist, with some posing significant risks. Detecting malignant skin lesions, particularly those with pigmentation, necessitates advanced image detection techniques and computer classification capabilities. In the proposed model, we have used the HAM10000 dataset, comprising 10,015 images, for enhancing the accuracy of skin cancer detection. We have carefully selected a subset of this dataset and implemented augmentation techniques to improve the model's precision. Our analysis focuses on the CNN- based model. Our proposed system achieved an outstanding validation accuracy of 97.92 % with the CNN model. This research contributes to the early identification of specific categories of skin diseases, empowering medical practitioners to validate and administer appropriate treatments
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