Semantic Segmentation System of Pigmented Skin Lesions Based on Convolutional Neural Networks

Currently, one of the most common types of malignant neoplasms in humans is skin cancer. There has been a need for automated and reliable approaches for accurate and rapid clinical detection and skin cancer diagnosis. The development of artificial intelligence-based automated assistive diagnostic tools for early detection of skin cancer on dermatoscopic images can help to reduce melanoma-induced mortality. Image segmentation is a key step in automated diagnostic systems for pigmented skin lesions. This paper presents a neural network system of semantic segmentation for pigmented skin lesions on dermatoscopic images based on the U-Net convolutional neural network. The simulation results showed that the proposed system allows detecting and segmenting pigmented lesions with an accuracy of 93.32%. The use of neural network segmentation as a stage of pre-processing of dermatoscopy images allows minimizing the influence of the patient's skin color type, the level of illumination, and the resulting occlusions in the presence of hair structures. The proposed system prepares dermatoscopic images for further analysis for automated classification of pigmented skin lesions.