Fast and accurate mobile-aided screening system of moderate diabetic retinopathy

Elloumi, Yaroub; Mbarek, Manef Ben; Boukadida, R.; Akil, Mohamed; Bedoui, Sammy

doi:10.1117/12.2588505

Cited by 13 publications

(8 citation statements)

References 43 publications

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“…This approach offers the possibility of applying the proposed technique across various datasets, in order to perform an accurate feature extraction. The same DL extension can be adopted for the segmentation of artery and vein, which is crucial to detect several retinal components [12] or pathologies such hypertensive retinopathy [13], cataract [14], diabetic retinopathy [15] and aged macular degeneration [16].…”

Section: Discussionmentioning

confidence: 99%

Retinal blood vessel segmentation from high resolution fundus image using deep learning architecture

boudegga,

Elloumi,

Ben Abdallah

et al. 2023

ESANN 2023 Proceesdings

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The Retinal Vascular Tree (RVT) segmentation is required to diagnose various ocular pathologies. Recently, fundus images are acquired with higher resolution, which allows representing a large range of vessel thickness. However, standard Deep Learning (DL) architectures with static and small convolution size have failed to achieve higher segmentation performance. In this paper, we propose a novel DL architecture for RVT segmentation dedicated for high resolution fundus images. The idea consists at extending the U-net architecture by increasing (e.g. decreasing) convolution kernel size through convolution blocs, in correlation with downscale (e.g. upscale) of feature map dimensions. The proposed architecture is validated on HRF database, where average sensitivity is increased from 56% to 84%.

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Section: Discussionmentioning

confidence: 99%

Retinal blood vessel segmentation from high resolution fundus image using deep learning architecture

boudegga,

Elloumi,

Ben Abdallah

et al. 2023

ESANN 2023 Proceesdings

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“…Furthermore, we aim to extend our framework to consider other ocular pathologies, such as diabetic retinopathy, glaucoma or aged macular degeneration. 56,57…”

Section: Discussionmentioning

confidence: 99%

“…Consequently, the framework may be implemented in smartphone devices to be provided as a mobile‐aided screening system for cataract grading, which will widely increase the accessibility to eye diagnosis. Furthermore, we aim to extend our framework to consider other ocular pathologies, such as diabetic retinopathy, glaucoma or aged macular degeneration 56,57 …”

Section: Discussionmentioning

confidence: 99%

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 algorithms can outperform human assessment for target diseases and have triggered veri cation failure (9-13) with higher accuracies (14) than human ground truthing despite the aid of optical coherence tomography (OCT) (15). The technology is also more accessible (16- 19), 200-fold faster (20,21), and capable of sub-clinical detection (3,(22)(23)(24). Although designed to function autonomously in parallel with established clinical ow, task shifting of retinal screening remains unrealised due to ethical concerns and missed incidental disease.…”

Section: Introduction Screening With Deep Learningmentioning

confidence: 99%

Ocular Biomarkers: Useful Incidental Findings by Deep Learning Algorithms in Retinal Photographs

Martin

Cook²,

Frost³

et al. 2023

Preprint

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Background/Objectives: Ocular biomarkers can provide immediate and non-invasive indications of ocular and systemic health but are underutilised due to the paucity and inequitable distribution of eyecare professionals. Deep learning analysis of colour fundus photographs has task shifting potential to efficiently differentiate ocular biomarkers, as well as providing earlier diagnosis, additional reach via telehealth, and ultimately improving population health. The study aimed to explore the clinical implications arising from deep learning detection of non-target retinal biomarkers in colour fundus photographs. Subjects/Methods: Patients referred for treatment-resistant hypertension were imaged between 2016 and 2022 at a specialty clinic in Perth, Australia. The same 45° colour retinal photograph selected for each of the 433 participants imaged was processed by three deep learning algorithms. All positive results for diabetic retinopathy in non-diabetic participants were graded by two expert retinal specialists. Results: A total of 29 non-diabetic participants were flagged as positive for diabetic retinopathy by deep learning algorithms. Of these, 28 (96.6%) had clinically significant non-target retinal pathology likely to benefit from early intervention. The algorithms with more target diseases captured less incidental disease. All three algorithms demonstrated a correlation between false positive diabetic retinopathy results and severity of hypertensive retinopathy. Conclusions: The findings indicate that existing deep learning models can identify additional pathologies likely to benefit from early intervention within an at-risk, hypertensive cohort, and have potential for immediate clinical application in other populations. The findings also support a pivotal pathway toward autonomous comprehensive screening.

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Fast and accurate mobile-aided screening system of moderate diabetic retinopathy

Cited by 13 publications

References 43 publications

Retinal blood vessel segmentation from high resolution fundus image using deep learning architecture

Retinal blood vessel segmentation from high resolution fundus image using deep learning architecture

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

Ocular Biomarkers: Useful Incidental Findings by Deep Learning Algorithms in Retinal Photographs

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