Enhancing skin cancer detection with explainable artificial intelligence: A customized extended deep U-shaped encoder decoder network approach

One of the most common types of cancer globally is skin cancer. Quick identification of this cancer plays a significant role in patient treatment and successful recovery. It is a condition characterized by the uncontrolled growth of skin cells. Skin cancer consists of two broad categories: melanoma and non-melanoma, each with distinct characteristics and treatment approaches. Recently, deep convolutional techniques have contributed high-quality models to the automation system for the segmentation, visualization, and detection of skin cancer. A deep convolutional neural network architecture, known as U-shaped encoder-decoder network (U-net), is used in various fields, mainly in medical image segmentation. It is also used for various tasks like image processing and computer vision. We proposed a customized extended deep U-net architecture with configured layer dissemination for the classification and segmentation of each image with an affected area and visually explainable visibility of skin cancer consisting of two categorical data samples: melanoma and non-melanoma. The model achieved high accuracy.