Detection and location of microaneurysms in fundus images based on improved YOLOv4 with IFCM

Abstract

Diabetic retinopathy (DR) is one of the leading causes of blindness for people suffering from diabetes. Microaneurysm (MA) is the initial symptom of DR. MA is a lesion based disease which starts as small red spots on the retina and increases in size as the DR progresses which finally leads to blindness. So eliminating the lesion can effectively prevent DR at an early stage. However, due to complex retinal structure, different brightness and contrast of fundus images with effects of factors such as different patients, environment changes, and difference in acquisition equipment, it is difficult for existing detection algorithms to achieve accurate results of MA detection and location. Therefore, the detection algorithm of improved YOLOv4 (YOLOv4‐Pro) was proposed. First, an improved Fuzzy C‐Means (IFCM) clustering algorithm was proposed to optimize anchor parameters of target samples to improve matching results between anchors and feature graphs. In order to control noise and improve efficiency, a median filtering method was employed to update the criterion function of the original FCM algorithm, and a K‐means algorithm was employed to initialize clustering. Second, a SENet attention module was added in the backbone of YOLOv4 to enhance key information and suppress background, improving the confidence of MA effectively. Finally, the spatial pyramid pooling (SPP) module was added to the neck to enhance the acceptance domain of the output characteristics of the backbone network, and profits separating of important context information. The improved YOLOv4 with IFCM was verified on the Kaggle DR dataset and compared with other methods. Experimental results show that optimizing the prior frame with the IFCM algorithm can make it suitable to frame the Kaggle DR dataset, which improves the detection accuracy of the network by nearly 5%, and provides a nice performance on detection and location of MA in fundus images. This would help ophthalmologists finding the exact location of MA on retina, thereby simplifying the process and eliminating any manual intervention.

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“…Object detection algorithms of deep learning could be broadly classified into two types: single-stage and two-stage detection algorithms. Single-stage algorithms, such as the YOLO series [23][24][25][26][27] and SSD [28,29], make direct predictions of the categories and positions of targets. Two-stage algorithms are employed by the R-CNN series [30][31][32][33], which includes R-CNN, Fast R-CNN, Faster R-CNN, and Mask-R-CNN.…”