Ocular diseases diagnosis in fundus images using a deep learning: approaches, tools and performance evaluation

Elloumi, Yaroub; Akil, Mohamed; Boudegga, Henda

doi:10.1117/12.2519098

Cited by 25 publications

(13 citation statements)

References 19 publications

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“…The diagnosis of ocular pathology using fundus images is a significant difficulty in health care [ 1 ]. Ocular disease refers to any condition or disorder that interferes with the eye's capacity to operate correctly or has a detrimental impact on the eye's visual acuity [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for Ocular Disease Recognition: An Inner-Class Balance

Khan

Tafshir

Alam

et al. 2022

Computational Intelligence and Neuroscience

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It can be challenging for doctors to identify eye disorders early enough using fundus pictures. Diagnosing ocular illnesses by hand is time-consuming, error-prone, and complicated. Therefore, an automated ocular disease detection system with computer-aided tools is necessary to detect various eye disorders using fundus pictures. Such a system is now possible as a consequence of deep learning algorithms that have improved image classification capabilities. A deep-learning-based approach to targeted ocular detection is presented in this study. For this study, we used state-of-the-art image classification algorithms, such as VGG-19, to classify the ODIR dataset, which contains 5000 images of eight different classes of the fundus. These classes represent different ocular diseases. However, the dataset within these classes is highly unbalanced. To resolve this issue, the work suggested converting this multiclass classification problem into a binary classification problem and taking the same number of images for both classifications. Then, the binary classifications were trained with VGG-19. The accuracy of the VGG-19 model was 98.13% for the normal (N) versus pathological myopia (M) class; the model reached an accuracy of 94.03% for normal (N) versus cataract (C), and the model provided an accuracy of 90.94% for normal (N) versus glaucoma (G). All of the other models also improve the accuracy when the data is balanced.

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Section: Introductionmentioning

confidence: 99%

Deep Learning for Ocular Disease Recognition: An Inner-Class Balance

Khan

Tafshir

Alam

et al. 2022

Computational Intelligence and Neuroscience

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“…Then, the V x i ð Þ vector is provided to the meta classifier B to generate the y i input image cataract stage, as in Equation (8).…”

Section: Ensemble Learning Framework For Cataract Gradingmentioning

confidence: 99%

“…In fact, several Deep Learning (DL) architectures are dedicated to the classification problem, which is varied in terms of processing principles, and so in terms of classification results. 8 Our main idea consists of stacking features of DL architectures in order to provide higher performance grading. The second challenge is to ensure such grading even using a small dataset.…”

Section: Introductionmentioning

confidence: 99%

“…The first challenge consists of ensuring higher accuracy when classifying images into cataract stages. In fact, several Deep Learning (DL) architectures are dedicated to the classification problem, which is varied in terms of processing principles, and so in terms of classification results 8 . Our main idea consists of stacking features of DL architectures in order to provide higher performance grading.…”

Section: Introductionmentioning

confidence: 99%

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Cataract grading method based on deep convolutional neural networks and stacking ensemble learning

Elloumi

2022

Int J Imaging Syst Tech

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The cataract is the most common cause of severe vision impairment or blindness worldwide. A periodical diagnosis is recommended in order to prevent cataract severity, where screening might be feasibly ensured through fundus images. In this paper, we propose a CAD system to efficiently grade the cataract from fundus images. For such a need, an ensemble learning framework is put forward where the knowledge of three convolutional deep neural networks is stacked in order to perform an efficient cataract prediction. The main contributions of this work are given as follows: (1) Preprocessing and data augmentation of fundus images are carried out to ensure the robustness of the cataract grading; (2) The well‐known DL architectures (Inception‐V3, MobileNet‐V2 and NasNet‐Mobile) are fine‐tuned and learned as base classifiers; (3) An Extreme Learning Machine (ELM) is performed as a meta classifier to stack the predictions of base classifiers. The evaluation is conducted using a dataset of 590 fundus images selected from two public databases. The suggested framework achieves 94.07% accuracy, 94.37% sensitivity, 93.62% specificity, 95.71% precision and 95.04% F‐measure for cataract grading. The experimentation proves that the proposed framework successfully grades fundus images into cataract severity. Moreover, stacking ensemble learning allows achieving a performance that significantly surpasses the ones realized by each DL architecture, applied separately.

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“…Deep learning-based methods have been previously proposed to detect ocular pathologies, e.g. 18,19,20 . For transfer learning, a pre-trained Inception-v3 model 21 is considered here similar to the work previously reported in reference 1 .…”

Section: Designing the Networkmentioning

confidence: 99%

A deep learning-based smartphone app for real-time detection of five stages of diabetic retinopathy

Majumder

Elloumi

Akil

et al. 2020

Real-Time Image Processing and Deep Learning 2020

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This paper presents the real-time implementation of deep neural networks on smartphone platforms to detect and classify diabetic retinopathy from eye fundus images. This implementation is an extension of a previously reported implementation by considering all the five stages of diabetic retinopathy. Two deep neural networks are first trained, one for detecting four stages and the other to further classify the last stage into two more stages, based on the EyePACS and APTOS datasets fundus images and by using transfer learning. Then, it is shown how these trained networks are turned into a smartphone app, both Android and iOS versions, to process images captured by smartphone cameras in real-time. The app is designed in such a way that fundus images can be captured and processed in real-time by smartphones together with lens attachments that are commercially available. The developed real-time smartphone app provides a costeffective and widely accessible approach for conducting first-pass diabetic retinopathy eye exams in remote clinics or areas with limited access to fundus cameras and ophthalmologists. Keywords: Real-time implementation of deep neural networks on smartphones, real-time smartphone app for detection and classification of diabetic retinopathy, first-pass eye exam by smartphone app.

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Ocular diseases diagnosis in fundus images using a deep learning: approaches, tools and performance evaluation

Cited by 25 publications

References 19 publications

Deep Learning for Ocular Disease Recognition: An Inner-Class Balance

Deep Learning for Ocular Disease Recognition: An Inner-Class Balance

Cataract grading method based on deep convolutional neural networks and stacking ensemble learning

A deep learning-based smartphone app for real-time detection of five stages of diabetic retinopathy

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