Blood vessel segmentation using deep learning architectures for aid diagnosis of diabetic retinopathy

Elaouaber, Z.A.; Feroui, Amel; Lazouni, Mohammed El Amine; Messadi, Mahammed

doi:10.1080/21681163.2022.2145999

Cited by 5 publications

(5 citation statements)

References 21 publications

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“…The results obtained on the DRIVE and HRF datasets were compared alongside three studies by the researchers in [20,22,41]. As mentioned earlier, the researchers in [41] employed a DNN for retinal vasculature segmentation.…”

Section: Evaluation and Discussionmentioning

confidence: 99%

“…As mentioned earlier, the researchers in [41] employed a DNN for retinal vasculature segmentation. Elaouaber [20] used three deep learning models, which included SegNet, U-Net, and CNN, to achieve the same. They obtained the best results using SegNet, whereas the researchers in [22] used a context-involved U-Net approach for retinal vasculature extraction.…”

Section: Evaluation and Discussionmentioning

confidence: 99%

“…Gargari et al [17] U-Net++ DRIVE, MESSIDOR 2022 Roychowdhury et al [11] Gaussian mixture model classifier DRIVE, CHASEDB1, STARE 2014 Fan et al [10] Image matting DRIVE, CHASEDB1, STARE 2018 Memari et al [9] Fuzzy c means clustering DRIVE, CHASEDB1, STARE 2019 Zhang et al [22] U-Net DRIVE, CHASE-DB1, STARE, HRF 2022 Atli and Gedik [16] Sine-Net DRIVE, CHASEDB1, STARE 2021 Sathananthavathi et al [18] U-Net CHASE DB1, DRIVE, STARE, HRF 2021 Deng and Ye [26] D-MNet CHASE DB1, DRIVE, STARE, HRF 2022 Elaouaber et al [20] Multiple DL models DRIVE, CHASE-DB1, HRF 2022…”

Section: Study Methods Dataset(s) Yearmentioning

confidence: 99%

“…An enhanced U-Net was employed for retinal vasculature extraction by the researchers in [19]. For the same task, three deep learning models, including SegNet, U-Net, and Convolutional Neural Network, were utilized by the researchers in [20]. Among these, SegNet was found to be the most effective.…”

Section: Diabetic Retinopathy Retinal Vasculature Segmentationmentioning

confidence: 99%

See 3 more Smart Citations

GAN-Based Approach for Diabetic Retinopathy Retinal Vasculature Segmentation

Sebastian,

Elharrouss,

Al-Maadeed

et al. 2023

Bioengineering

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Most diabetes patients develop a condition known as diabetic retinopathy after having diabetes for a prolonged period. Due to this ailment, damaged blood vessels may occur behind the retina, which can even progress to a stage of losing vision. Hence, doctors advise diabetes patients to screen their retinas regularly. Examining the fundus for this requires a long time and there are few ophthalmologists available to check the ever-increasing number of diabetes patients. To address this issue, several computer-aided automated systems are being developed with the help of many techniques like deep learning. Extracting the retinal vasculature is a significant step that aids in developing such systems. This paper presents a GAN-based model to perform retinal vasculature segmentation. The model achieves good results on the ARIA, DRIVE, and HRF datasets.

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Section: Evaluation and Discussionmentioning

confidence: 99%

Section: Evaluation and Discussionmentioning

confidence: 99%

Section: Study Methods Dataset(s) Yearmentioning

confidence: 99%

Section: Diabetic Retinopathy Retinal Vasculature Segmentationmentioning

confidence: 99%

See 2 more Smart Citations

GAN-Based Approach for Diabetic Retinopathy Retinal Vasculature Segmentation

Sebastian,

Elharrouss,

Al-Maadeed

et al. 2023

Bioengineering

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“…The presented technique is composed of 5 different stages they are the localization of fovea, pre-processing, segmenting of the optic disc, classification, feature extracting process and detection of blood vessels. In [14], the author concentrated on the application of different DL approaches for precise retinal vasculature semantic segmentation. The author has exploited 3 distinct methods: CNN, UNet, and SegNet.…”

Section: Literature Reviewmentioning

confidence: 99%

Blood Vessel Segmentation and Classification for Diabetic Retinopathy Grading Using Dandelion Optimization Algorithm with Deep Learning Model

2023

IJIES

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Diabetic retinopathy (DR) is a diabetic complexity that mainly affects the eye. Generally, an ophthalmologist defines the severity of the retinopathy by directly inspecting colour images and estimating them by visually examining the fundus. Due to the enormous amount of diabetic patients all over the world, it becomes an expensive process. The automated system was designed for accurate recognition of the disease using segmentation and fundus image. The blood vessel segmentation process is used to identify and separate blood vessels from the surrounding tissues in an image. This is a crucial stage in the detection of DR, as it enables physicians to measure and identify changes in blood vessels that are indicative of the disease. Numerous approaches are used for blood vessel segmentation and DR classification, along with deep learning (DL) and machine learning (ML) techniques. In this article, we introduce a novel dandelion optimization algorithm with deep learning based blood vessel segmentation and classification (DOADL-BVSC) model for DR grading. The presented DOADL-BVSC technique involves the concepts of blood vessel segmentation and DL-based classification for DR diagnosis on retinal fundus photographs. To accomplish this, the presented DOADL-BVSC technique uses the fuzzy set type-II approach for the image enhancement process. Next, U-Net with Bi-directional feature pyramid network (U-BFPN) model is exploited to segment the blood vessels in the retinal imaging effectively. Moreover, a squeeze and excitation (SE) network is applied for feature vector generation with DOA based hyperparameter optimizer. Finally, quantum autoencoder (QAE) approach is utilized for identifying and classifying DR into distinct stages. The experimental analysis of the DOADL-BVSC approach is investigated on benchmark DR datasets. The simulation results depicted the superior results of the DOADL-BVSC technique with increased accuracy of 99.93%.

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