基于增强特征融合YOLOV5的视网膜病变检测

Retinal disease detection based on enhanced feature fusion YOLOV5

针对视网膜黄斑病变数据集缺失以及视网膜图像冗余度过大问题,建立了包含3种视网膜病变的视网膜黄斑疾病检测数据集,并提出了一种基于改进YOLOV5的视网膜病变检测模型。该模型在特征提取网络中引入了改进的注意力机制模块,突出病变区域,降低视网膜图像中大量背景的影响。其次,改进加强特征提取网络,加权融合具有大量细节信息的浅层特征,增强网络对视网膜病变的定位能力。实验结果表明,本文模型具有良好的视网膜病变检测效果,检测精度达97.3%。

In view of the lack of retinopathy dataset and the excessive redundancy of retinal images,a retinopathy detection dataset is established,including three kinds of retinopathy,and a retinopathy detection model is proposed based on improved YOLOV5.The model introduces an improved attention mechanism module into the feature extraction network to highlight the lesion area and reduce the influence of large numbers of background in the retinal image,and further,improves and enhances the feature extraction network,weights and fuses shallow features with a large amount of detail information,and enhances the ability of the network to locate retinopathy.The experimental results show that the model proposed in this paper has good effect for detecting retinopathy,and the detection accuracy is 97.3%.