Research On Medical Image Recognition And Segmentation Technology Based On Deep Learning

With the development of artificial intelligence technology and the explosive accumulation of medical image data,applying deep learning technology to medical image analysis to help doctors complete diagnosis and treatment more quickly is of great significance to the development of the medical field.A doctor’s routine diagnosis largely depends on his own subjective experience,and the imaging quality of medical images and the complex background environment will also have a greater impact on the doctor’s judgment.Object detection and recognition and semantic segmentation in medical images are important research contents in the field of medical image processing.Its main purpose is to detect and recognize specific objects in images and assist doctors in the clinical diagnosis and surgical treatment of patients.In this paper,the detection recognition and segmentation of medical images is the research object,and the medical image processing algorithm based on deep learning is proposed by analyzing and improving the existing algorithms,and the main contributions and work of this paper are as follows:(1)Aiming at the problem of less medical image data and difficult labeling,a medical image data processing algorithm AMIP(Active Medical Image Process)based on active learning is proposed to process medical image data.Based on the active learning framework,the algorithm can intelligently allocate unlabeled samples,select only complex samples for manual labeling,and reduce labeling costs;combine positioning stability and positioning tightness to classify unlabeled samples,and add noise processing;An active learning noise sample selection mechanism is proposed to balance the sample instability problem caused by extreme noise samples.Experiments show that this method can provide great help for medical image data annotation and model pre-training.(2)Aiming at the difficult problem of medical image detection and positioning,a new medical image detection method Yolo XT(You Only Look Once X with Transformer)is proposed to realize the detection and recognition of medical image targets.The algorithm introduces a new attention mechanism so that the network model can adaptively strengthen important features and suppress unimportant features;a new feature fusion architecture is proposed to fuse multiscale features to obtain comprehensive context Semantic information;a positive and negative sample selection strategy is proposed to improve the imbalance between positive and negative samples caused by the complex background interference of medical images.Experiments show that the algorithm shows excellent performance in medical image detection tasks.(3)Aiming at the precise segmentation of high-complexity,low-contrast organ and tissue images,a deep learning-based precise medical image segmentation algorithm SWTRU(Star-shaped Window Transformer Reinforced U-Net)is proposed.This algorithm provides a new idea for the combination of U-Net and Transformer,inheriting the fast-learning ability of U-Net and the good generalization ability of Transformer.In SWTRU,a star-shaped window self-attention mechanism is proposed,which can effectively expand the attention area while controlling the calculation cost and achieve the effect of global attention;a new full-scale skip connection mechanism is designed to learn multi-level semantic features;A feature fusion mechanism is proposed to achieve feature dimensionality reduction and fusion to reduce the amount of calculation.Experimental results prove that the method has good performance in precision medical image segmentation tasks.The research content of this article can help doctors quickly and accurately analyze and diagnose diseases,reduce the risk of misdiagnosis and missed diagnosis,and provide an important basis for clinical applications such as improving the efficiency and safety of surgery and adjusting treatment plans in the later stage.