A Deep Learning-Based Unified Framework for Red Lesions Detection on
Retinal Fundus Images
- URL: http://arxiv.org/abs/2109.05021v1
- Date: Fri, 10 Sep 2021 00:12:13 GMT
- Title: A Deep Learning-Based Unified Framework for Red Lesions Detection on
Retinal Fundus Images
- Authors: Norah Asiri, Muhammad Hussain, Fadwa Al Adel, Hatim Aboalsamh
- Abstract summary: Red-lesions, i.e., microaneurysms (MAs) and hemorrhages (HMs) are the early signs of diabetic retinopathy (DR)
Most of the existing methods detect only MAs or only HMs because of the difference in their texture, sizes, and morphology.
We propose a two-stream red lesions detection system dealing simultaneously with small and large red lesions.
- Score: 3.5557219875516646
- License: http://creativecommons.org/publicdomain/zero/1.0/
- Abstract: 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.
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