Diabetic retinopathy techniques in retinal images: A review

Salamat, Nadeem; Missen, Malik Muhammad Saad; Rashid, Ayesha

doi:10.1016/j.artmed.2018.10.009

Cited by 95 publications

(45 citation statements)

References 86 publications

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“…There are two machine learning paradigms to try to solve the problem: the traditional classification approach where the input is a feature vector obtained from the fundus images [7][8][9], and the deep learning approach (in particular, using convolutional neural networks) [10][11][12]. While the second approach usually gets better classification results, most of these methods do not provide understandable interpretations about the relevant features of the different pathological signs in the retina, so its clinical usefulness is questionable until more research efforts in the interpretation of the high-level features extracted by the convolutional blocks of a CNN will be done.…”

Section: Exudates Hemorrhagesmentioning

confidence: 99%

Detection of Early Signs of Diabetic Retinopathy Based on Textural and Morphological Information in Fundus Images

Colomer

Igual

Naranjo

2020

Sensors

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Estimated blind people in the world will exceed 40 million by 2025. To develop novel algorithms based on fundus image descriptors that allow the automatic classification of retinal tissue into healthy and pathological in early stages is necessary. In this paper, we focus on one of the most common pathologies in the current society: diabetic retinopathy. The proposed method avoids the necessity of lesion segmentation or candidate map generation before the classification stage. Local binary patterns and granulometric profiles are locally computed to extract texture and morphological information from retinal images. Different combinations of this information feed classification algorithms to optimally discriminate bright and dark lesions from healthy tissues. Through several experiments, the ability of the proposed system to identify diabetic retinopathy signs is validated using different public databases with a large degree of variability and without image exclusion.

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Section: Exudates Hemorrhagesmentioning

confidence: 99%

Detection of Early Signs of Diabetic Retinopathy Based on Textural and Morphological Information in Fundus Images

Colomer

Igual

Naranjo

2020

Sensors

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“…on the surface of the retina in the human eye [4]. They are very minute with a diameter range of 10µm to 100µm [5].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Automated Detection of Microaneurysms in Fundus Images Using Feature-Set Tuning

Usman

Almejalli

2020

IEEE Access

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Due to the widespread of diabetes mellitus and its associated complications, a need for early detection of the leading symptoms in the masses is felt like never before. One of the earliest signs is the presence of microaneurysms (MAs) in the fundus images. This work presents a new technique for automatic detection of MAs in color fundus images. The proposed technique utilizes Genetic Programming (GP) and a set of 28 selected features from the preprocessed fundus images in order to evolve a mathematical expression. Through the binearization of the fitness scores, the optimal expression is evolved generation by generation through a stepwise enhancement process. The best expression is then used as a classifier for real world applications. Experimental results using three publically available datasets validate the usefulness of the proposed technique and its ability to outperform the state of the art contemporary approaches. INDEX TERMS Automatic microaneurysms detection, diabetic retinopathy, fundus images, genetic programming.

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“…DR is one of the usual retinal diseases, which is generally, a widespread problem of diabetes. Diabetes is the fundamental cause of blindness to several 20 to 65 years people that leads to the occurrences of skin, hearing, stroke, kidney‐related risks, diabetic neuropathy, foot, and heart‐related problems 1–3 . The DR seems to have two main types: proliferative diabetic retinopathy (PDR) and non‐proliferative diabetic retinopathy (NPDR) 4,5 .…”

Section: Introductionmentioning

confidence: 99%

Automatic diagnosis of diabetic retinopathy with the aid of adaptive average filtering with optimized deep convolutional neural network

Roshini¹,

Ravi²,

Mathew³

et al. 2020

Int J Imaging Syst Tech

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The most effective treatment for diabetic retinopathy (DR) is the early detection through regular screening, which is critical for a better prognosis. Automatic screening of the images would assist the physicians in diagnosing the condition of patients easily and accurately. This condition searches out for special importance of image processing technology in the way of processing the retinal fundus images. Accordingly, this article plans to develop an automatic DR detection model with the aid of three main stages like (a) image preprocessing, (b) blood vessel segmentation, and (c) classification. The preprocessing phase includes two steps: conversion of RGB to Lab, and contrast enhancement. The Histogram equalization process is done using the contrast enhancement of an image. To the next of preprocessing, the segmentation phase starts with a valuable procedure. It includes (a), thresholding the contrast‐enhanced and filtered images, (b) thresholding the keypoints of contrast‐enhanced and filtered images, and (c) adding both thresholded binary images. Here, the filtering process is performed by proposed adaptive average filtering, where the filter coefficients are tuned or optimized by an improved meta‐heuristic algorithm called fitness probability‐based CSO (FP‐CSO). Finally, the classification part uses Deep CNN, where the improvement is exploited on the convolutional layer, which is optimized by the same improved FP‐CSO. Since the conventional CSO depends on a fitness probability in the improved algorithm, the proposed algorithm termed as FP‐CSO. Finally, valuable comparative and performance analysis has confirmed the effectiveness of the proposed model.

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Diabetic retinopathy techniques in retinal images: A review

Cited by 95 publications

References 86 publications

Detection of Early Signs of Diabetic Retinopathy Based on Textural and Morphological Information in Fundus Images

Detection of Early Signs of Diabetic Retinopathy Based on Textural and Morphological Information in Fundus Images

Intelligent Automated Detection of Microaneurysms in Fundus Images Using Feature-Set Tuning

Automatic diagnosis of diabetic retinopathy with the aid of adaptive average filtering with optimized deep convolutional neural network

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