DOTHE based image enhancement and segmentation using U-Net for effective prediction of human skin cancer

Skin cancer is a disorder that is becoming more prevalent around the world and is responsible for numerous mortality. Skin cancer starts in one organ and slowly moves to other parts of the body before killing the patient. Early detection of skin cancer is important for reducing the number of deaths all over the world. Because it takes time and money to manually diagnose skin cancer, it is critical to create automated diagnostic techniques to categorize skin lesions more accurately. Medical image enhancement and deep learning-based segmentation techniques are developed in this proposed work. Cancer affected and non-affected skin images are given as input for the proposed method. Data collection consists of raw data that cannot produce high accuracy. So, a certain pre-processing technique is used in the proposed method to achieve high accuracy. Dingo Optimized Texture based Histogram Equalization (DOTHE) strategy is utilized to improve the skin image. Then the pre-processed image is partitioned into different parts or regions according to the features and properties of the pixels in the image. U-Net network architecture is used in the proposed method to segment the enhanced image. The performances of the proposed model are analyzed using the Convolutional Neural Network (CNN) model. This proposed model is tested with several metrics which attain better performances like 97% accuracy, 96% sensitivity, 95% specificity, 94% precision, and 3% error. Thus the designed model enhances and segments the image effectively, and it is useful for effective skin cancer prediction.