A Deep Learning-Based Unified Framework for Red Lesions Detection on Retinal Fundus Images.

Red-lesions, i.e., microaneurysms (MAs) and hemorrhages (HMs), are the early signs of diabetic retinopathy (DR). The automatic detection of MAs and HMs on retinal fundus images is a challenging task. Most of the existing methods detect either only MAs or only HMs because of the difference in their texture, sizes, and morphology. Though some methods detect both MAs and HMs, they suffer from the curse of dimensionality of shape and colors features and fail to detect all shape variations of HMs such as flame-shaped HM. Leveraging the progress in deep learning, we proposed a two-stream red lesions detection system dealing simultaneously with small and large red lesions. For this system, we introduced a new ROIs candidates generation method for large red lesions fundus images; it is based on blood vessel segmentation and morphological operations, and reduces the computational complexity, and enhances the detection accuracy by generating a small number of potential candidates. For detection, we adapted the Faster RCNN framework with two streams. We used pre-trained VGGNet as a bone model and carried out several extensive experiments to tune it for vessels segmentation and candidates generation, and finally learning the appropriate mapping, which yields better detection of the red lesions comparing with the state-of-the-art methods. The experimental results validated the effectiveness of the system in the detection of both MAs and HMs; the method yields higher performance for per lesion detection according to sensitivity under 4 FPIs on DiaretDB1-MA and DiaretDB1-HM datasets, and 1 FPI on e-ophtha and ROCh datasets than the state of the art methods w.r.t. various evaluation metrics. For DR screening, the system outperforms other methods on DiaretDB1-MA, DiaretDB1-HM, and e-ophtha datasets.