基于改进YOLOv5的幽门螺杆菌免疫印迹图像识别

Western Blot Image Recognition of Helicobacter Pylori Based on Improved YOLOv5

针对幽门螺杆菌免疫印记图像重度依赖医生目测识别,存在效率低、速度慢等问题,提出一种基于改进YOLOv5的幽门螺杆菌免疫印迹图像检测模型。首先对YOLOv5的特征提取器进行优化,采用DenseNet作为新的特征提取器来解决梯度消失问题;然后通过限制最高下采样倍数,使得模型对小目标检测更加灵敏;最后引入Swish激活函数代替原YOLOv5中的LeakyReLU激活函数并改进Io U来获取更加准确的边界信息。在幽门螺杆菌免疫印记图像数据集上验证改进后的模型检测能力,实验结果表明,改进后模型的F1-score高达0.93、mAP@0.5达95.4%、mAP@0.5:0.95达75.6%、每秒检测帧数达54fps,满足临床上检测时限要求。

To solve the problems of low efficiency and slow speed of Helicobacter pylori immunoblot images that heavily rely on visual recognition by physicians,an improved YOLOv5-based Helicobacter pylori immunoblot image detection model is proposed.Firstly,the feature extractor of YOLOv5 is optimized,and Dense Net is used as a new feature extractor to solve the problem of gradient disappearance. Then,by limiting the maximum downsampling multiple,the model is more sensitive to small target detection. Finally,the Swish activation function is introduced to replace the original YOLOv5 LeakyReLU activation function and improve Io U to obtain more accurate boundary information. The detection ability of the improved model is verified in the Helicobacter pylori immunoimprint image data set. The experimental results show that the F1-score of the improved model is as high as0. 93,mAP@ 0. 5 up to 95. 4%,mAP@ 0. 5 : 0. 95 up to 75. 6%,and the detection frames per second is 54 fps,which can meet the clinical detection time limit requirements.