Efficient Skin Disease Diagnosis Using Optimized nnU-Net Segmentation and Hybrid E-Cap Net with UFO-Net

Abstract

Skin diseases are among the most frequent and pervasive conditions affecting individuals all over the world. The two primary causes of skin cancer are climate change and global warming. If skin conditions are not identified and treated promptly, they may become fatal. Advanced ML and DL approaches on skin diseases often face limitations such as insufficient data diversity, high variability in imaging quality, and challenges in accurately distinguishing between similar-looking conditions. These drawbacks can lead to reduce diagnostic accuracy and generalizability of the models. To overcome the aforementioned challenges, an improved segmentation and hybrid deep learning approach is used to identify numerous kinds of skin disease. Initially, raw images for input are collected from the skin disease image dataset. The collected image is pre-processed with resizing and a Hierarchical Noise Deinterlace Net (HNDN) to remove noise. The pre-processed images are then segmented into different parts or regions using the no new U-Network (nnU-Net). Here, the Marine Predator Algorithm (MPA) is used to choose the nnU-Net learning rate, and batch size optimally. Then, the segmented image is subjected to a hybrid Efficient-capsule network (E-cap Net) and Unified force operation network (UFO-Net) classifier predicting several types of skin disease. An analysis of proposed method’s simulation results indicates that it achieves 97.49% accuracy, 90.06% precision, and 98.56% selectivity. Thus, the proposed method is a most effective method for predicting the multi-type skin disease.