Deep Learning based Skin Cancer Detection and Classification

Skin cancer is a serious and widespread type of cancer that requires early and precise detection for proper treatment and positive patient results. (CNNs) that comes under Deep Learning, has proven to be a powerful tool for automated skin cancer classification from medical images, but their lack of interpretability has hindered their adoption in clinical settings. In this report we have proposed integrating techniques like eXplainable Artificial Intelligence (XAI) like LIME and occlusion techniques with CNN models to address this challenge. The performance of the proposed framework will be assessed using various architectures of CNN, including ResNet50, InceptionV3, MobileNetV2, and VGG16, which will be fine-tuned to detect and classify skin cancer. We aim to yield higher accuracy for skin cancer diagnosis while also enhancing the interpretability and transparency of the decision-making process by proposing the combined excellence of deep learning and XAI. This approach aims to foster trust and facilitate the integration of automated systems into clinical practice, ultimately improving patient care.