Research On Cardiac Image Segmentation And Recognition Based On Artificial Neural Network

In the current field of medical image diagnosis,cardiac medical image segmentation is a crucial step.Traditional cardiac image segmentation techniques typically rely on manual feature extraction and rule generation based on reinforcement learning,which require human intervention and domain-specific expertise.Therefore,these methods often result in inadequate segmentation accuracy,weak model robustness,and require significant human resources and time investment,making it difficult to meet the demands of large-scale data processing.This study primarily focuses on the performance of two improved deep learning models in cardiac medical image segmentation tasks.To address the shortcomings of the UT-Net model in capturing multi-scale information,improvements were made.Firstly,a spatial pyramid pooling module was added to capture contextual information at different scales.This module helps enhance the model’s performance in handling multi-scale targets.Secondly,an attention mechanism was introduced to focus on more important regions during the feature extraction process.This mechanism helps improve the model’s ability to recognize boundaries between different targets and backgrounds.Finally,dilated convolutions were employed to expand the receptive field and capture more contextual information.Through these improvements,the model achieved better performance in cardiac medical image segmentation tasks.To better segment the ventricular and atrial regions,a cardiac medical image segmentation algorithm based on an improved Swin U-Net was proposed.Firstly,Swin Transformer was used as the basic feature extraction module to capture long-range dependencies.Swin Transformer combines self-attention mechanism and local perception,enabling the model to handle large-sized targets.To further enhance performance,multi-scale features extracted by Mask R-CNN were utilized,and improvements were made to the residual connections in Swin U-Net.Through these enhancements,the Swin U-Net model exhibited superior performance in cardiac medical image segmentation tasks.Experimental validation demonstrated that both proposed improvement methods achieved favorable performance in cardiac medical image segmentation tasks.By studying these two improved deep learning models,the experimental results indicate that these methods are highly efficient in extracting detailed features and learning feature relationships,providing valuable insights for the field of cardiac medical image segmentation.