Explainable AI Insights into Skin Cancer Detection: A Comparative Study of CNN, DenseNet, and ResNet

Skin cancer is a dangerous and widespread conditionthat requires early and accurate detection for effective treatment. Recent advancements in deep learning have demonstrated promise in the detection of skin cancer from image datasets. This research aims to analyze the effectiveness of different models in detecting skin cancer, including DenseNet, CNN, and ResNet. This study evaluates the metrics like accuracy, precision, recall, and F1-score in identifying skin cancer. Additionally, this study investigates the important features in the images that lead to the model prediction using Explainable AI - LIME and SHAP. The ultimate aim is to discover clever and accurate methods for identifying skin cancer early. This helps patients get treatment quickly when it matters most.