Implementation of Medical Image Segmentation Algorithm Based on Deep Network

Image segmentation is the process of dividing an image into multiple regions based on specific features, such as grayscale, color, texture, and shape. Medical image segmentation refers to separating the area of the lesion from the surrounding environment. By extracting relevant features and semantic information, it provides a reliable basis for clinical diagnosis and pathology research, assists doctors in making more accurate diagnoses. However, medical image datasets are limited by fewer sources, accurate labeling, and often contain noise and artifacts, making segmentation more difficult than semantic segmentation of natural images. In recent years, the emergence of deep learning technology solves the above problems very well.This paper discusses the segmentation methods for skin lesion and fundus retinal vessel images using deep learning techniques. The skin lesion dataset primarily includes skin conditions such as seborrheic keratosis, melanoma and benign moles. The fundus retinal vessel dataset is mainly sourced from diabetic retinopathy screening. First, both datasets are pre-processed with rotation, cropping, and grayscale. The U2-Net is used as the base model for training, which has proven to have great potential in the field of medical image segmentation. In addition, attention mechanisms are introduced between jump connections. Channel attention focuses on the feature distribution on the three channels of RGB, and spatial attention concentrates on the feature distribution of specific positions on the channel. Finally, a transformer module is adopted to enhance feature extraction. These improvements significantly enhance feature extraction capabilities, improving segmentation accuracy and efficiency. Experimental results indicate that this model performs well on both training datasets and exhibits good generalization when tested on other datasets. Therefore, there is no doubt that this design of deep learning network architectures plays a crucial role in the segmentation of medical images.