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Most DL approaches are unable to offer uncertainty quantification (UQ) for the output, resulting in overconfidence and excessive sample deviation. The dataset was contributed by the International Skin Image Collaboration (ISIC) Archive. To deal with uncertainty during skin cancer image classification and disappearing gradients, we used Monte-Carlo (MC) dropout with the ResNet 50 DL model. This assists in overcoming the problem of vanishing gradients. After the preprocessing, the training was completed, yielding a 94% accuracy rate. The MC dropout method identifies the out-of-domain data, reduces the training size which results in an accuracy of 89.9%. As a result, the ResNet50 model for classifying skin cancer achieved 89% of accuracy. 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