Quality assessment of eye fundus images taken by wide-view non-mydriatic cameras

Carrillo, Cesar; Calderón, Gustavo; López, Osvaldo; Nakano, Marko; Pérez-Meana, Héctor; Pérez, A. García; Quiroz, Hugo

doi:10.1109/ropec48299.2019.9057034

Cited by 5 publications

(5 citation statements)

References 14 publications

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“…The EyeQ was developed from the EyePACS dataset (https://www.kaggle.com/c/diabetic-retinopathy-detection) to provide a fundus image quality assessment through a multiple color‐space fusion network (MCF‐Net) based on ResNet121 38,39 . The EyeQ can help to assess the image quality with three labels: reject, usable, and good.…”

Section: Methodsmentioning

confidence: 99%

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Development of preprocessing methods and revised EfficientNet for diabetic retinopathy detection

Lin

Jiang

2023

Int J Imaging Syst Tech

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Section: Methodsmentioning

confidence: 99%

“…multiple color-space fusion network (MCF-Net) based on ResNet121. 38,39 The EyeQ can help to assess the image quality with three labels: reject, usable, and good. In general, the dataset only includes usable-and good-quality fundus images to train and test the performance of the DR grading model.…”

Section: Assess Quality Of the Fundus Imagementioning

confidence: 99%

Development of preprocessing methods and revised EfficientNet for diabetic retinopathy detection

Lin

Jiang

2023

Int J Imaging Syst Tech

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“…3) Assess quality of the fundus image: In order to obtain the most important features from fundus images, this study adopts the Eye-Quality library [13,14] to assess the image quality with three labels: reject, usable, and good (Fig. 4).…”

Section: ) Remove the Black Border Of The Fundus Imagementioning

confidence: 99%

Development of Revised ResNet-50 for Diabetic Retinopathy Detection

Lin

2023

Preprint

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Diabetic retinopathy (DR) produces bleeding, exudation, and new blood vessel formation conditions. DR can damage the retinal blood vessels and cause vision loss or even blindness. If DR is detected early, ophthalmologists can use lasers to create tiny burns around the retinal tears to inhibit bleeding and prevent the formation of new blood vessels, in order to prevent deterioration of the disease. Imaging examination is a valuable technique for doctors in the prediction and treatment of diseases. The rapid improvement of deep learning has made image recognition an effective technology; it can avoid misjudgments caused by different doctors’ evaluations and help doctors to predict the condition quickly. Therefore, this paper adopts visualization and preprocessing in the ResNet-50 model to improve module calibration, to enable the model to predict DR accurately.

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“…Their research used multiple databases and obtained 99.8% AUC through the SVM classifier. Carrillo et al [12] analysed several generic features, such as the statistical features of histograms, cooccurrence matrices, run-length and cumulative probability of blur detection. Javidi et al [13] presented the morphological component analysis based framework.…”

Section: Related Workmentioning

confidence: 99%

“…Carrillo et al. [12] analysed several generic features, such as the statistical features of histograms, cooccurrence matrices, run‐length and cumulative probability of blur detection. Javidi et al.…”

Section: Related Workmentioning

confidence: 99%

Blood vessel and background separation for retinal image quality assessment

Liu

et al. 2021

IET Image Processing

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Retinal image analysis has become an intuitive and standard aided diagnostic technique for eye diseases. The good image quality is essential support for doctors to provide timely and accurate disease diagnosis. This paper proposes an end‐to‐end learning based method for evaluating the retinal image quality. First, blood vessels of the input image are segmented by U‐Net, and the fundus image is divided into two parts: blood vessels and background. Then, we design a dual branch network module which extracts global features that influence the image quality and suppress the interference of blood vessels and local textures to achieve better performance. The proposed module can be embedded in various advanced network structures. The experimental results show the more efficient convergence rate for the network with the module. The best network accuracy rate is 85.83%, the AUC is 0.9296, and the F1‐score is 0.7967 on the collected local dataset. Additionally, the model generalization is tested on the public DRIMDB dataset. The accuracy, AUC, and F1‐score reach 97.89%, 0.9978, and 0.9688, respectively. Compared with the state‐of‐the‐art networks, the performance of the proposed method is proven to be accurate and effective for retinal image quality assessment.

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Quality assessment of eye fundus images taken by wide-view non-mydriatic cameras

Cited by 5 publications

References 14 publications

Development of preprocessing methods and revised EfficientNet for diabetic retinopathy detection

Development of preprocessing methods and revised EfficientNet for diabetic retinopathy detection

Development of Revised ResNet-50 for Diabetic Retinopathy Detection

Blood vessel and background separation for retinal image quality assessment

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