面向2D医学图像检测的YOLO算法研究综述OA

In recent years,the breakthrough development of artificial intelligence technology has promoted a paradigm change in the interdisciplinary field of medicine and engineering,among which the object detection algorithm based on deep learning has shown significant advantages in medical image analysis.As a typical representative of the single-stage detection framework,the YOLO(you only look once)series algorithms have demonstrated unique advantages of high real-time,strong generalization ability and precise positioning in the field of medical image analysis through the"end-to-end"detection paradigm,and have gradually become the mainstream research methods for lesion detection,cell recognition and other tasks.The research of YOLO improved algorithm for medical object detection is sorted out.Firstly,based on the dimension of algorithm architecture innovation,the core evolution path of 12 generations of basic algorithms from YOLOv1 to YOLOv11 is sorted out,and the improvement breakthroughs,advantages and limitations,and medical scene performance of each version of YOLO are compared and analyzed.Secondly,the classic open-source datasets in the field of medical object detection are summarized,and the commonly used evaluation indicators in object detection are expounded.At the same time,the literature research on the use of YOLO improved algorithm in the detection of cervical cells,blood cells,pulmonary nodules and diabetic retinopathy in 2D medical images is reviewed,and different improved methods are comprehensively compared and analyzed.Finally,this paper summarizes the medical scenarios corresponding to different improvement ideas of YOLO,and discusses the challenges and future development directions in this field.