Research On Red Blood Cell Recognition And Detection Based On Deep Learning

In the field of modern biomedicine,the identification and detection of red blood cells helps to assist in the diagnosis of related diseases and can provide pathologists with valuable information.Therefore,it is considered to be one of the most important clinical examination tests.At present,there are still manual methods for counting and diagnosing different types of red blood cells in clinical practice,but the detection efficiency of this method is low and the recognition accuracy depends on the status and experience of the staff,which affects the objective judgment of the medical staff on the condition to a certain extent..In recent years,how to apply deep learning to the medical field has become a research hotspot.This article is based on image processing technology,starting from the needs of actual detection,and launching research by introducing deep learning technology and methods.Aiming at the current problems of low accuracy and efficiency of red blood cell detection,this paper designs a lightweight YOLOv4 network model,which avoids the complex feature extraction process,and can still maintain a good level of overlap and density of red blood cell images.Detection accuracy and efficiency are suitable for deployment on processing and detection platforms with limited computing power.In the research process,the YOLOv4 algorithm was first selected as the basic network model structure,and the k-means algorithm was used to optimize the anchor point box to obtain the anchor points of the potential red blood cells to be identified.Secondly,by changing the network infrastructure of YOLOv4,the original The backbone network CSPDarknet53 integrates the lightweight MobileNetv3 network into the YOLOv4 structure,and replaces the standard convolution in the PANet module with a deep separable convolution,which achieves a significant reduction in network parameters and calculations.Then,by optimizing the activation function of the backbone network,Adjust the parameters to balance the detection accuracy and speed,and finally complete the design of the lightweight YOLOv4 network.Through experimental comparison,it is proved that the lightweight model has better detection accuracy and speed for red blood cells,as well as strong generalization ability.It can be suitable for deployment in equipment with limited computing power and storage performance.At the same time,the morphological characteristics of red blood cells As an effective means of medical diagnosis and evaluation,the precise identification and detection of the biomedicine has far-reaching significance for biomedical research.