Mobile assisted diabetic retinopathy detection using deep neural network

Suriyal, Shorav; Druzgalski, C.; Gautam, Kumar

doi:10.1109/gmepe-pahce.2018.8400760

Cited by 38 publications

(27 citation statements)

References 2 publications

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“…The work described in [11] applies a convolution filter to make images more consistent and smoother, with a size equal to 525. The work described in [17] proposed a pipeline where the first step leads to enhance the retina by deblurring the Smartphone-captured fundus image.…”

Section: Automatic Methods For Retinal Abnormality Detection 421 Prmentioning

confidence: 99%

“…Thereafter, the results are returned to the subjects. In the second approach, an intelligent software tool is integrated on the cloud-platform which insures an automatic detection [11,13,19]. Another approach consists at performing the detection on the Smartphone as an all-inone device for ophthalmology mobile computer-aided-system [12,18].…”

Section: Flowchart Of Retinal Abnormality Detectionmentioning

confidence: 99%

“…In [11], the main objective consists at screening the diabetic retinopathy using a neural network with mobileNets model. First, the images in the dataset are resized from 492823264 to 2562256 pixels.…”

Section: Detection Processingmentioning

confidence: 99%

“…The app is compared with retinograph-captured fundus images where the detection accuracy is about 87%. In [11], the "MobileNets" CNN is configured to classify smartphone captured fundus images on two classes (Dr or no DR) where final accuracy is about 73%.…”

Section: Detection Processingmentioning

confidence: 99%

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Detection of retinal abnormalities using smartphone-captured fundus images: a survey

Akil

Elloumi

2019

Real-Time Image Processing and Deep Learning 2019

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Several retinal pathologies lead to severe damages that may achieve vision lost. Moreover, some damages require expensive treatment, other ones are irreversible due to the lack of therapies. Therefore, early diagnoses are highly recommended to control ocular diseases. However, early stages of several ocular pathologies lead to the symptoms that cannot be distinguish by the patients. Moreover, ageing population is an important prevalence factor of ocular diseases which is the cases of most industrial counties. Further, this feature involves a lake of mobility which presents a limiting factor to perform periodical eye screening. Those constraints lead to a late of ocular diagnosis and hence important ocular pathology patients are registered. The forecast statistics indicates that affected population will be increased in coming years. Several devices allowing the capture of the retina have recently been proposed. They are composed by optical lenses which can be snapped on Smartphone, providing fundus images with acceptable quality. Thence, the challenge is to perform automatic ocular pathology detection on Smartphone captured fundus images that achieves higher performance detection while respecting timing constraint with respect to the clinical employment. This paper presents a survey of the Smartphonecaptured fundus image quality and the existing methods that use them for retinal structures and abnormalities detection. For this purpose, we first summarize the works that evaluate the Smartphone-captures fundus image quality and their FOV (field-of-view). Then, we report the capability to detect abnormalities and ocular pathologies from those fundus images. Thereafter, we propose a flowchart of processing pipeline of detecting methods from Smartphone captured fundus images and we investigate about the implementation environment required to perform the detection of retinal abnormalities.

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Section: Automatic Methods For Retinal Abnormality Detection 421 Prmentioning

confidence: 99%

Section: Flowchart Of Retinal Abnormality Detectionmentioning

confidence: 99%

Section: Detection Processingmentioning

confidence: 99%

Section: Detection Processingmentioning

confidence: 99%

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Detection of retinal abnormalities using smartphone-captured fundus images: a survey

Akil

Elloumi

2019

Real-Time Image Processing and Deep Learning 2019

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“…For real-time assistance of diabetic patients, Suriyal et al [16] developed a mobile app for the identification of DR on the basis of deep learning. The designed application was based on tensor flow DNN architecture which trained and validated on fundus images.…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection of Diabetic Retinopathy: A Review on Datasets, Methods and Evaluation Metrics

et al. 2020

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Diabetic retinopathy (DR) is a fast-spreading disease across the globe, which is caused by diabetes. The DR may lead the diabetic patients to complete vision loss. In this scenario, early identification of DR is more essential to recover the eyesight and provide help for timely treatment. The detection of DR can be manually performed by ophthalmologists and can also be done by an automated system. In the manual system, analysis and explanation of retinal fundus images need ophthalmologists, which is a timeconsuming and very expensive task, but in the automated system, artificial intelligence is used to perform an imperative role in the area of ophthalmology and specifically in the early detection of diabetic retinopathy over the traditional detection approaches. Recently, numerous advanced studies related to the identification of DR have been reported. This paper presents a detailed review of the detection of DR with three major aspects; retinal datasets, DR detection methods, and performance evaluation metrics. Furthermore, this study also covers the author's observations and provides future directions in the field of diabetic retinopathy to overcome the research challenges for the research community. INDEX TERMS Artificial intelligence, deep learning, diabetic retinopathy, fundus images, machine learning, ophthalmology.

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End-to-End Mobile System for Diabetic Retinopathy Screening Based on Lightweight Deep Neural Network

Elloumi

Abroug

Bedoui

2022

Lecture Notes in Computer Science

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Diabetic Retinopathy (DR) is the leading cause of visual impairment among working-aged adults. Screening and early diagnosis of DR is essential to avoid visual acuity reduction and blindness. However, a worldwide limited access to ophthalmologists may prevent an early diagnosis of this blinding condition. In this paper, we propose a novel method for screening DR from smartphone-captured fundus images. The main challenges are to perform higher accurate detection even with reduced quality of handheld captured fundus images and to provide the result into the smartphone used for acquisition. For such a need, we apply transfer learning to the lightweight deep neural network "NasnetMobile" which is used as a feature descriptor, while configuring a multilayer perceptron classifier to deduce the DR disease, in order to take benefit from their lower complexity. A dataset composed of 440 fundus images is structured, where the acquisition and statement are performed by expert ophthalmologists. A cross-validation process is conducted where 95.91% accuracy, 94.44% sensitivity, 96.92% specificity and 95.71% precision in average are achieved. In addition, the whole processing flowchart is implemented into a mobile device, where the execution time is under one second whatever the fundus image is. Those performances allow deploying the proposed system in a clinical context.

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Mobile assisted diabetic retinopathy detection using deep neural network

Cited by 38 publications

References 2 publications

Detection of retinal abnormalities using smartphone-captured fundus images: a survey

Detection of retinal abnormalities using smartphone-captured fundus images: a survey

Automatic Detection of Diabetic Retinopathy: A Review on Datasets, Methods and Evaluation Metrics

End-to-End Mobile System for Diabetic Retinopathy Screening Based on Lightweight Deep Neural Network

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