Automated Detection System for Diabetic Retinopathy Using Two Field Fundus Photography

Kumar, Poninder; Deepak, R.; Sathar, Anuja; Sahasranamam, V.; Kumar, Rajesh

doi:10.1016/j.procs.2016.07.237

Cited by 41 publications

(21 citation statements)

References 18 publications

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“…A. Pre-processing Pre-processing is an important step in process of detecting DR. Most of the researchers convert retinal fundus image into green channel [1][2][3], [5][6][7][8], [10][11][12][13][14] as a contrast of MAs and HMs are high in this channel. Contrast limited adaptive histogram equalization (CLAHE) is applied to enhance the image and then median filter to remove noise [4, 6, 7 and 10].…”

Section: Methodsmentioning

confidence: 99%

“…The Morphological operation followed by thresholding is applied to detect [2,12] blood vessels. Retinal fundus image is converted into HSV color image then applied bi-cubic interpolation, gamma correction, median filter, and thresholding technique to segment blood vessels [6]. Match filter and first-order derivation of Gaussian matched filter technique are applied in [8].…”

Section: Methodsmentioning

confidence: 99%

“…Figure 1 shows both of the symptoms. EXs are nothing but some fluid, oily by nature [7] and appearing as yellowish regions [6]. This all symptoms are detectable using the fundus image of the retina.…”

Section: Introductionmentioning

confidence: 96%

“…The early stage of DR is Microaneurysms (MAs) which are formed due to swelling of small blood vessels in the retina. Hemorrhages (HMs), hard exudates (EXs) and soft exudates (EXs) are the next signs of the Non-Proliferative Diabetic Retinopathy [6]. Fig.…”

Section: Introductionmentioning

confidence: 99%

“…Shirbahadurkar et al [5] used morphological operation and Gabor filter for feature extraction and then classification using Support vector machine and feed-forward neural network. Sharath et al [6] detect optic disc, blood vessels and white/yellow lesions using different image processing methods and for detection of red lesions, they removed all detected features like an optic disc, blood vessels and white/yellow lesion from the pre-processed. Dilip et al [7] applied pre-processing, extract features like blood vessels area, optic disc area, EXs area, HMs area, MAs area, Shannon entropy for classification and quantitative analysis.…”

Section: Introductionmentioning

confidence: 99%

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Detection of Diabetic Retinopathy: A Survey

Panchal¹

2018

IJRASET

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Diabetic Retinopathy (DR) is a severe condition for the diabetic patient in their eye ailment. This issue can be detected from the retinal fundus image. Microaneurysms (MAs), Hemorrhages (HMs) and Exudates (EXs) are three features of nonproliferative diabetic retinopathy. Microaneurysms are the earlier stage of DR. There are many methods or techniques proposed till date for detection of the disease in the eye from the retinal fundus image. This paper aims to survey some techniques to detect blood vessels, optic disc exudates (EXs), microaneurysms (MAs) and Hemorrhages (HMs) and then classified using different classifier.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Detection of Diabetic Retinopathy: A Survey

Panchal¹

2018

IJRASET

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Diagnostic Accuracy of Community-Based Diabetic Retinopathy Screening With an Offline Artificial Intelligence System on a Smartphone

et al. 2019

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IMPORTANCE Offline automated analysis of retinal images on a smartphone may be a cost-effective and scalable method of screening for diabetic retinopathy; however, to our knowledge, assessment of such an artificial intelligence (AI) system is lacking. OBJECTIVE To evaluate the performance of Medios AI (Remidio), a proprietary, offline, smartphone-based, automated system of analysis of retinal images, to detect referable diabetic retinopathy (RDR) in images taken by a minimally trained health care worker with Remidio Non-Mydriatic Fundus on Phone, a smartphone-based, nonmydriatic retinal camera. Referable diabetic retinopathy is defined as any retinopathy more severe than mild diabetic retinopathy, with or without diabetic macular edema. DESIGN, SETTING, AND PARTICIPANTS This prospective, cross-sectional, population-based study took place from August 2018 to September 2018. Patients with diabetes mellitus who visited various dispensaries administered by the Municipal Corporation of Greater Mumbai in Mumbai, India, on a particular day were included. INTERVENTIONS Three fields of the fundus (the posterior pole, nasal, and temporal fields) were photographed. The images were analyzed by an ophthalmologist and the AI system. MAIN OUTCOMES AND MEASURES To evaluate the sensitivity and specificity of the offline automated analysis system in detecting referable diabetic retinopathy on images taken on the smartphone-based, nonmydriatic retinal imaging system by a health worker. RESULTS Of 255 patients seen in the dispensaries, 231 patients (90.6%) consented to diabetic retinopathy screening. The major reasons for not participating were unwillingness to wait for screening and the blurring of vision that would occur after dilation. Images from 18 patients were deemed ungradable by the ophthalmologist and hence were excluded. In the remaining participants (110 female patients [51.6%] and 103 male patients [48.4%]; mean [SD] age, 53.1 [10.3] years), the sensitivity and specificity of the offline AI system in diagnosing referable diabetic retinopathy were 100.0% (95% CI, 78.2%-100.0%) and 88.4% (95% CI, 83.2%-92.5%), respectively, and in diagnosing any diabetic retinopathy were 85.2% (95% CI, 66.3%-95.8%) and 92.0% (95% CI, 97.1%-95.4%), respectively, compared with ophthalmologist grading using the same images. CONCLUSIONS AND RELEVANCE These pilot study results show promise in the use of an offline AI system in community screening for referable diabetic retinopathy with a smartphone-based fundus camera. The use of AI would enable screening for referable diabetic retinopathy in remote areas where services of an ophthalmologist are unavailable. This study was done on patients with diabetes who were visiting a dispensary that provides curative services to the population at the primary level. A study with a larger sample size may be needed to extend the results to general population screening, however.

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Taylor series‐based deep belief network for automatic classification of diabetic retinopathy using retinal fundus images

Athalye

Vijay

2021

Int J Imaging Syst Tech

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The diagnosis of diabetic retinopathy (DR) disease in the early stage is very important to reduce the risk in DR treatment. Different methods are in practice for detecting the lesions automatically with the retinal image. However, detecting the occurrence of exudates in the macular region poses a challenging task in the computer‐assisted diagnosis of DR. A robust and computationally efficient model for localizing the lesions and features in the retinal fundus image is processed in this research by proposing the Taylor‐based deep belief network (T‐based DBN) classifier. Exudates and their contours are determined based on the blood vessel and optic disc segmentation model. The microaneurysms are detected based on the wavelet model, and the lesions are segmented with the thresholding and binarization approach. The proposed T‐based DBN is highly effective in classifying the DR, based on the multiple layers associated with the restricted Boltzmann machines (RBM) and multi‐layer perceptron (MLP) layer. The proposed T‐based DBN is the integration of the Taylor series with the DBN classifier. The proposed T‐based DBN produces an accurate detection rate and yields better theoretical error bounds. The performance revealed by the proposed model is evaluated using the metrics, namely specificity, sensitivity, and accuracy, with the values of 90.757%, 92.225%, and 92.122%, respectively.

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Automated Detection System for Diabetic Retinopathy Using Two Field Fundus Photography

Cited by 41 publications

References 18 publications

Detection of Diabetic Retinopathy: A Survey

Detection of Diabetic Retinopathy: A Survey

Diagnostic Accuracy of Community-Based Diabetic Retinopathy Screening With an Offline Artificial Intelligence System on a Smartphone

Taylor series‐based deep belief network for automatic classification of diabetic retinopathy using retinal fundus images

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