Authors - Nandini Babbar, Anshika Shreshth, Saswati Gogoi, Sunil Kumar Abstract - Early and precise detection of skin cancer is very necessary, as it is one of the most aggressive diseases in the world, and its effective treatment is required. Because many skin cancer types appear visually similar and the available datasets are imbalanced, accurate diagnosis of skin lesions remains difficult using current medical technologies. Melanoma, one of the most severe skin cancer diseases, has a very low survival rate. In this paper, a multimodal is developed for classifying skin cancer by combining saliency maps with EfficientNetB3.This research work uses PAD-UFES-20 dataset to access and train the model. The clinicians can understand the lesion better through saliency maps, as they provide insightful information about the model’s decision-making process. This work concludes how deep learning models can be useful in improving skin cancer classification using an efficient approach for early detection clinically.
