In the clinical stage, suspected hemolytic plasma may cause hemolysis illness, manifesting as symptoms such as heart failure, severe anemia, etc. Applying a deep learning method to plasma images significantly improves recognition accuracy, so that this paper proposes a plasma quality detection model based on improved “You Only Look Once” 5th version (YOLOv5). Then the model presented in this paper and the evaluation system ?were introduced? into the plasma datasets, and ?the average accuracy of the final classification reached 98.7%?. The results of this paper's experiment were obtained through the combination of several key algorithm modules including? omni-dimensional dynamic convolution, pooling with separable kernel attention, residual bi-fusion feature pyramid network, ?and? re-parameterization convolution. The method of this paper? obtains the feature information of spatial mapping efficiently, and enhances the average recognition accuracy of plasma quality detection. This paper presents a high-efficiency detection method for plasma images, aiming to provide a practical approach to prevent hemolysis illnesses caused by external factors.
Coronary artery segmentation is a critical step in the clinical diagnosis of coronary heart disease. To tackle segmentation breaks and false segmentation caused by the thin and complex coronary vessels as well as the severe foreground-background imbalance in computed tomography angiography images, this paper proposes MSCNet, a coronary artery segmentation network with multi-scale cascade encoding and dynamic spatial context enhancement. The network constructed a multi-scale cascaded encoder using Swin Transformer and large-kernel convolutions. It sequentially modeled and fused multi-scale features by capturing long-range dependencies and local details, and reparameterized large-kernel convolutions via a spatial frequency matrix to strengthen fine detail capture. Meanwhile, a spatial transformer module was designed to dynamically guide multi-head attention learning and optimize decoding performance. On the ImageCAS dataset, MSCNet achieved an average Dice coefficient of 81.24%, which was 3.57%, 3.78%, and 3.85% higher than 3D UX-Net, SwinUNETR, and SegMamba, respectively. MSCNet effectively improves the accuracy of coronary artery segmentation and provides support for clinical evaluation.