&lt;p&gt;Digital image processing software for diagnosing diabetic retinopathy from fundus photograph&lt;/p&gt;

Ratanapakorn, Tanapat; Daengphoonphol, Athiwath; Eua-Anant, Nawapak; Yospaiboon, Yosanan

doi:10.2147/opth.s195617

Cited by 19 publications

(6 citation statements)

References 14 publications

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“…The article by Tanapat Ratanapakorn, et al, 2019, detect the DR by classifying it into three classes i.e. normal eye, PDR, and NPDR, using the combination of digital image processing toolbox of MATLAB [ 151 ]. The experiments are performed on fundus images and achieve good accuracy.…”

Section: Dr Screening Methodsmentioning

confidence: 99%

Recent developments on computer aided systems for diagnosis of diabetic retinopathy: a review

Dubey

Dixit

2022

Multimed Tools Appl

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Diabetes is a long-term condition in which the pancreas quits producing insulin or the body’s insulin isn’t utilised properly. One of the signs of diabetes is Diabetic Retinopathy. Diabetic retinopathy is the most prevalent type of diabetes, if remains unaddressed, diabetic retinopathy can affect all diabetics and become very serious, raising the chances of blindness. It is a chronic systemic condition that affects up to 80% of patients for more than ten years. Many researchers believe that if diabetes individuals are diagnosed early enough, they can be rescued from the condition in 90% of cases. Diabetes damages the capillaries, which are microscopic blood vessels in the retina. On images, blood vessel damage is usually noticeable. Therefore, in this study, several traditional, as well as deep learning-based approaches, are reviewed for the classification and detection of this particular diabetic-based eye disease known as diabetic retinopathy, and also the advantage of one approach over the other is also described. Along with the approaches, the dataset and the evaluation metrics useful for DR detection and classification are also discussed. The main finding of this study is to aware researchers about the different challenges occurs while detecting diabetic retinopathy using computer vision, deep learning techniques. Therefore, a purpose of this review paper is to sum up all the major aspects while detecting DR like lesion identification, classification and segmentation, security attacks on the deep learning models, proper categorization of datasets and evaluation metrics. As deep learning models are quite expensive and more prone to security attacks thus, in future it is advisable to develop a refined, reliable and robust model which overcomes all these aspects which are commonly found while designing deep learning models.

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Section: Dr Screening Methodsmentioning

confidence: 99%

Recent developments on computer aided systems for diagnosis of diabetic retinopathy: a review

Dubey

Dixit

2022

Multimed Tools Appl

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“…When defining the level of damage caused by DR, the International Clinical Diabetic Retinopathy (ICDR) disease severity scale is the gold standard [3]. From no DR (level 0) to proliferative DR (level 4) on a five-point scale, the standard proposes non-proliferative DR (level 3) [4][5][6]. Microaneurysms (M.A.)…”

Section: A Background Of Diabetic Retinopathymentioning

confidence: 99%

Segmentation Using the IC2T Model and Classification of Diabetic Retinopathy Using the Rock Hyrax Swarm-Based Coordination Attention Mechanism

Jagadesh,

Karthik,

Siri

et al. 2023

IEEE Access

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Diabetic Retinopathy (DR) evaluations are increasingly being automated using artificial intelligence. Diabetes-related retinal vascular disease is a major cause of blindness and visual impairment worldwide. Therefore, automated DR detection devices would greatly aid in reducing visual impairment due to DR through early screening and treatment. Researchers have provided many techniques for picking out abnormalities in retinal images during the past several years. In the past, automated methods for diagnosing diabetic retinopathy required a human to extract information from retinal images before passing them on to a classifier. This study takes a novel two-pronged approach to automated DR classification to solve the issues. Due to the low positive instance percentage of existing asymmetric, we segment O.D.s and B.V.s with an enhanced version of an improved contoured convolutional transformer (IC2T). We develop a contoured optical disc (OD), a blood vessels (BV) detection module, and a dual convolutional transformer block that combines local and global contexts to make trustworthy associations. A second-stage Improved Coordination Attention Mechanism (ICAM) network is trained to recognize retinal biomarkers for DR such as microaneurysms (M.A.), haemorrhages (H.M.), and exudates (EX). With an average accuracy of 96%, 97%, and 98% on EyePACS-1, Messidor-2, and DIARETDB0, respectively, the suggested technique has proven itself to be at the field's cutting edge. Comprehensive testing and comparisons to established methods support the proposed strategy.

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“…Technically, it is known that noise enters the retina fundus images in many cases, which needs to be removed. For this, researchers either normalize the images or use methods that would remove the non-uniform illumination [20,21]. Researchers have given empirical evidence that the green channel (RGB colour model) is the most appropriate for extracting red and yellow spots of the eyes [22].…”

Section: Role Of Image Processingmentioning

confidence: 99%

“…By improving the contrast, the difference between the different intensities is increased, and for the segmentation algorithm, it becomes easy to segment that area. Many authors have demonstrated in their work that the variation in the background can be reduced by image smoothing filters and histogram equalization methods [20]. Researchers are also using shade correction methods for reducing non-uniform illumination areas in the eyes [24].…”

Section: Role Of Image Processingmentioning

confidence: 99%

An Integrated Fundus Image Segmentation Algorithm for Multiple Eye Ailments

Datta

Das

Kumar

2021

IJEER

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The detection of eye illnesses requires a thorough inspection of all of the eye's structures. Most significantly, the presence of blood vessels, an optical disc, and any other unwelcome objects, if any are discovered, is critical in determining the type of eye disease present. Specifically, the goal of this research is to establish a thorough segmentation framework that will aid in the detection of anatomical anomalies in the eye. A novel segmentation technique for analyzing blood vessels, optical disc health, and the presence of exudates has been added into the software. - It was decided to add the detection of aberrant objects such as exudates into the algorithm in order to develop a generic segmentation method. In order to verify the accuracy of the segmentation at each stage, random sampling is employed at each stage. The segmentation is then validated using the intersection over union metric. The accuracy of the integrated segmentation method as a whole is 91.66 percent.

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<p>Digital image processing software for diagnosing diabetic retinopathy from fundus photograph</p>

Cited by 19 publications

References 14 publications

Recent developments on computer aided systems for diagnosis of diabetic retinopathy: a review

Recent developments on computer aided systems for diagnosis of diabetic retinopathy: a review

Segmentation Using the IC2T Model and Classification of Diabetic Retinopathy Using the Rock Hyrax Swarm-Based Coordination Attention Mechanism

An Integrated Fundus Image Segmentation Algorithm for Multiple Eye Ailments

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