Microaneurysm detection in fundus using improved Faster RCNN with CBAM integration
The detection of microaneurysms in fundus can effectively prevent and control diabet-ic retinopathy,and it has important clinical significance.However,the target area of this lesion is small and there is interference from other structures such as retinal bleeding.In addition,there are problems of uneven brightness and contrast in fundus images,which bring great challenges to the detection task.To address this problem,a method of microaneurysm detection based on Faster RCNN is proposed.First,the dataset is segmented based on the lesion to improve the pro-portion of the target area.Then,the backbone network is replaced with ResNet which has strong feature expression ability,and an attention mechanism is introduced to combine with FPN that joined fusion factor to perform multi-scale feature fusion,thereby improving the ability of the backbone network to extract feature information of small targets and increasing its attention to the target area.Experimental results show that the algorithm achieves detection results on the E-OphthaMA dataset,with a precision of 91.3％,a recall rate of 80.2％,which is 13.1％higher and 8％higher than the original model in terms of precision and recall rates respectively,and better detection results compared to other methods.
Small object detectionFaster RCNNMicroaneurysmAttention mechanismMulti-scale features fusion
NETL
NSTL
万方数据
