Classification of diabetic retinopathy with feature selection over deep features using nature-inspired wrapper methods

Canayaz, Murat

doi:10.1016/j.asoc.2022.109462

Cited by 43 publications

(16 citation statements)

References 59 publications

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“…(81,82) The development of innovative approaches continues. (83)(84)(85)(86)(87)(88)(89)(90)(91)(92)(93) The recent one presented by Zhang et al (83) is based on Deep Graph Correlation Network for grading. Researchers postulated that it has an accuracy near to retinal specialists and more than trained graders.…”

Section: Discussionmentioning

confidence: 99%

Is it time to consider teleophthalmology as a game-changer in the management of diabetic retinopathy?

Shahsuvaryan

2023

Revista Brasileira De Oftalmologia

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Section: Discussionmentioning

confidence: 99%

Is it time to consider teleophthalmology as a game-changer in the management of diabetic retinopathy?

Shahsuvaryan

2023

Revista Brasileira De Oftalmologia

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“…The proposed inception model achieves an overall accuracy rate of 98.7%, surpassing the present methods. Densenet-121, Xception, Inception-v3, Resnet-50 85.28% [46] Inception-ResNet-v2 72.33% [47] MobileNet_V2 93.09% [48] EfficientNet and DenseNet 96.32% [49] VGG16 96.86% [50] Hybrid Residual U-Net 94% [51] Inception-v3 88.1% Proposed Methodology Inception-V3 ( without using CLAHE + ESRGAN) Case 2 80.87% Inception-V3 (using CLAHE + ESRGAN) Case 1 98.7%…”

Section: Evaluation Considering a Variety Of Other Methodologiesmentioning

confidence: 99%

Deep Learning-Based Prediction of Diabetic Retinopathy Using CLAHE and ESRGAN for Enhancement

Alwakid¹,

Gouda²,

Humayun³

2023

Preprint

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Vision loss can be avoided if diabetic retinopathy (DR) is diagnosed and treated promptly. Following are the main 5 DR stages: none, moderate, mild, proliferate, and severe. In this study, a deep learning (DL) model is presented that diagnoses all 5 stages of DR with more accuracy than previous methods. The suggested method presents two scenarios: case 1 with image enhancement using contrast limited adaptive histogram equalization (CLAHE) filtering algorithm in conjunction with an Enhanced Super-resolution generative adversarial network (ESRGAN), and case 2 without image enhancement; augmentation techniques are then performed to generate a balanced dataset utilizing the same parameters for both cases. Using Inception-V3 applied to the Asia Pacific Tele-Ophthalmology Society (APTOS) datasets, the developed model achieved an accuracy of 98.7% for case 1 and 80.87% for case 2, which is greater than existing methods for detecting the five stages of DR. It was demonstrated that using CLAHE and ESRGAN improves a model's performance and learning ability.

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“…Jadhav et al [26] design an optimal feature selectionâ Ȃ Śbased diabetic retinopathy detection method which can develop automated DR detection by analyzing the retinal abnormalities like hard exudates, hemorrhages, Microaneurysm, and soft exudates. Canayaz et al [27] propose an approach based on feature selection with wrapper methods used for fundus images. Abdelmaksoud et al [28] introduce E-DenseNet, a hybrid deep learning method.…”

Section: A Retinal Image Classificationmentioning

confidence: 99%

Graph Adversarial Transfer Learning for Diabetic Retinopathy Classification

Jing-bo

Wang

et al. 2022

IEEE Access

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Diabetic retinopathy (DR) is an essential factor that has caused vision loss and even blindness in middle-aged and older adults. A system that can automatically perform DR diagnosis can help ophthalmologists save a lot of tedious work, such as DR grading or lesion detection. At the same time, patients can find their diseases earlier and perform the correct treatment. However, most of the existing methods require many DR annotations to train the model, and the DR data will vary to different degrees due to various shooting tools. The above problems lead to the inefficient use of existing data in the experiment, limiting actual deployment. To alleviate this problem, we propose a novel Graph Adversarial Transfer Learning (GATL) for DR diagnosis in a deep model through transfer learning, including intradomain alignment and inter-domain alignment. The proposed GATL enjoys several merits. First, our GATL adopts the self-supervised training to save the annotating cost in the target domain thus this domain adaptation method can significantly reduce annotation cost compared to the supervised approaches. Second, we introduce the graph neural network to extract potential features between unknown samples. Third, to enhance the robustness of the model, we use adversarial training to perform both inter-domain and intradomain alignment to further improve the model's classification accuracy. GATL achieved 94.3%, 97.5%, and 91.1% in accuracy, sensitivity, and specificity in the APTOS dataset and 92.7%, 95.7%, and 89.7% in the EyePACS dataset, respectively. Extensive experimental results on two challenging benchmarks, including APTOS 2019 and EyePACS, demonstrate that the proposed GATL performs favorably against baseline DR classification methods.

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Classification of diabetic retinopathy with feature selection over deep features using nature-inspired wrapper methods

Cited by 43 publications

References 59 publications

Is it time to consider teleophthalmology as a game-changer in the management of diabetic retinopathy?

Is it time to consider teleophthalmology as a game-changer in the management of diabetic retinopathy?

Deep Learning-Based Prediction of Diabetic Retinopathy Using CLAHE and ESRGAN for Enhancement

Graph Adversarial Transfer Learning for Diabetic Retinopathy Classification

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