A survey on segmentation of exudates and microaneurysms for early detection of diabetic retinopathy

Devaraj, Deepashree; Suma, R.; Kumar, Sandeep

doi:10.1016/j.matpr.2017.12.372

Cited by 13 publications

(7 citation statements)

References 9 publications

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“…Retinopathy is a condition that affects the eyes (DR). Its identification is necessary to alleviate vision loss and monitoring DR [ 43 ]. Jingyu Du et al [ 47 ], 2020, automatically detected MA from fundus images which involves candidate extraction and classification.…”

Section: Dr Screening Methodsmentioning

confidence: 99%

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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%

“…When vision is harmed, it is due to exudates, which are fluid leaks in the blood vessels (EXs). Exudates are divided into two types: hard exudates (yellow spots in the retina) and soft exudates (regions of soft yellow or white with smudged edges) [ 43 ]. Both the exudates are clearly defined in Fig.…”

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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“…Karkuzhali et al 8 presented another region growing-based technique for hemorrhage segmentation. The researchers performed two pre-processing methods: (1) Median filter used to remove noise and irregular illumination. (2) Sobel operator approach to identify the boundaries and gradient dimensions in retinal images.…”

Section: Retinal Lesion Segmentationmentioning

confidence: 99%

“…Retinal fundus imaging is a kind of image modality that can acquire the back of the eye that is, fundus eye to visualize the key structure that is, macula, optic disc, blood vessels, and retina. These fundus images observed by the expert ophthalmologists to determine the most significant DR indicators namely: microaneurysms, hemorrhages, hard and soft exudates 1 . These retinal DR variations are shown in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

UNIConv: An enhanced U‐Net based InceptionV3 convolutional model for DR semantic segmentation in retinal fundus images

Kaur

2022

Concurrency and Computation

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Segmentation of diabetic retinopathy is a significant requirement for correct severity estimation level. Due to dissimilarity in shapes, size, and quantity of retinal lesions, the procedure of labor-intensive type of grading becomes a more time-consuming and challenging task. It imposes the prerequisite for an automated segmentation framework that can accurately identify the regions with margins and aid ophthalmologists in the early detection and monitoring of the severity level. The proposed work introduces a customized U-Net model based on the InceptionV3 pre-trained CNN network. It uses a periodic shuffling approach along with sub-pixel convolution initialized to convolution nearest-neighbor resize. The model performed training and validation for non-proliferative diabetic retinopathy that is, microaneurysms, hemorrhages, hard and soft exudates on two standard datasets such as IDRiD and DIARETDB1. The proposed model achieves 99.82% accuracy, 88.39% sensitivity, and 99.94% specificity for hemorrhages, 99.95% accuracy, 70.76% sensitivity, and 99.98% specificity for microaneurysms, 99.83% accuracy, 85.9% sensitivity, and 99.95% specificity for hard exudates, along with 99.62% accuracy, 85.38% sensitivity, and 99.71% specificity for soft exudates segmentation on the IDRID dataset. In comparison with both datasets, the proposed system on the IDRID dataset performed accurately with superior state-of-the-art outcomes for segmentation of the retinal lesions.

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“…Caused mostly for diabetic patients, retinopathy is diabetic. For the extraction of diabetic retinopathy diseases, this study makes image segmentation [3]. Diabetes is a disease in which glucose metabolism abnormalities lead to increased blood glucose levels.…”

Section: Introductionmentioning

confidence: 99%

A Novel Filtered Segmentation-Based Bayesian Deep Neural Network Framework on Large Diabetic Retinopathy Databases

Akbar¹,

Midhunchakkaravarthy²

2020

RIA

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Image thresholding-based segmentation models play a vital role in the detection of Diabetic retinopathy (DR) on large databases. Most of the conventional segmentation-based classification models are independent of over segmented regions and outliers. Also, these models have less true positive rate and high error rate on different DR feature sets. In order to overcome these problems, a novel filtered based segmentation framework is designed and implemented on the large DR feature space. In this work, a novel image filtering approach, optimal image segmentation approach and hybrid Bayesian deep learning framework are developed on the large DR image databases. Experimental results proved that the proposed filtered segmentation-based Bayesian deep neural network has better accuracy and runtime than the conventional models on different DR variation databases.

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A survey on segmentation of exudates and microaneurysms for early detection of diabetic retinopathy

Cited by 13 publications

References 9 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

UNIConv: An enhanced U‐Net based InceptionV3 convolutional model for DR semantic segmentation in retinal fundus images

A Novel Filtered Segmentation-Based Bayesian Deep Neural Network Framework on Large Diabetic Retinopathy Databases

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