Indian Diabetic Retinopathy Image Dataset (IDRiD): A Database for Diabetic Retinopathy Screening Research

Porwal, Prasanna; Pachade, Samiksha; Kamble, Ravi; Kokare, Manesh; Deshmukh, Girish; Sahasrabuddhe, Vivek; Mériaudeau, Fabrice

doi:10.3390/data3030025

Cited by 610 publications

(318 citation statements)

References 24 publications

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“…The ATLAS dataset was established for developing automated algorithms for segmenting brain stroke lesions from structural MR images 19 . The IDRiD dataset was created to incite automated lesion detection algorithms from color fundus images with signs of diabetic retinopathy 20 . And the DRIVE database was established to advance automated segmentation algorithms of blood vessels from retinal images 21 .…”

Section: Background and Summarymentioning

confidence: 99%

“…The dataset provided in this work has a relatively large sample size in terms of medical image segmentation tasks (a total of 354 samples), as compared to a sample size of 81 in the IDRiD dataset 20 and a sample size of 40 in the DRIVE dataset 21 . However, in terms of TG classifications, this dataset is largely unbalanced and has a relatively limited sample size for certain categories, which may cause overfitting problems if used in a deep learning setting.…”

Section: Data Recordsmentioning

confidence: 99%

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The SUSTech-SYSU dataset for automatically segmenting and classifying corneal ulcers

et al. 2020

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Corneal ulcer is a common ophthalmic symptom. Segmentation algorithms are needed to identify and quantify corneal ulcers from ocular staining images. Developments of such algorithms have been obstructed by a lack of high quality datasets (the ocular staining images and the corresponding goldstandard ulcer segmentation labels), especially for supervised learning based segmentation algorithms. In such context, we prepare a dataset containing 712 ocular staining images and the associated segmentation labels of flaky corneal ulcers. In addition to segmentation labels for flaky corneal ulcers, we also provide each image with three-fold class labels: firstly, each image has a label in terms of its general ulcer pattern; secondly, each image has a label in terms of its specific ulcer pattern; thirdly, each image has a label indicating its ulcer severity degree. This dataset not only provides an excellent opportunity for investigating the accuracy and reliability of different segmentation and classification algorithms for corneal ulcers, but also advances the development of new supervised learning based algorithms especially those in the deep learning framework.

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Section: Background and Summarymentioning

confidence: 99%

Section: Data Recordsmentioning

confidence: 99%

The SUSTech-SYSU dataset for automatically segmenting and classifying corneal ulcers

et al. 2020

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“…The proposed model was trained using data from two publicly available datasets, IDRiD [23] and MESSIDOR [24]. Both these datasets are graded on a scale of three, where each grade is described as given in Table 1.…”

Section: Datasetsmentioning

confidence: 99%

DMENet: Diabetic Macular Edema diagnosis using Hierarchical Ensemble of CNNs

Singh

Gorantla

2020

PLoS ONE

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Diabetic Macular Edema (DME) is an advanced stage of Diabetic Retinopathy (DR) and can lead to permanent vision loss. Currently, it affects 26.7 million people globally and on account of such a huge number of DME cases and the limited number of ophthalmologists, it is desirable to automate the diagnosis process. Computer-assisted, deep learning based diagnosis could help in early detection, following which precision medication can help to mitigate the vision loss. Method: In order to automate the screening of DME, we propose a novel DMENet Algorithm which is built on the pillars of Convolutional Neural Networks (CNNs). DMENet analyses the preprocessed color fundus images and passes it through a two-stage pipeline. The first stage detects the presence or absence of DME whereas the second stage takes only the positive cases and grades the images based on severity. In both the stages, we use a novel Hierarchical Ensemble of CNNs (HE-CNN). This paper uses two of the popular publicly available datasets IDRiD and MESSIDOR for classification. Preprocessing on the images is performed using morphological opening and gaussian kernel. The dataset is augmented to solve the class imbalance problem for better performance of the proposed model. Results: The proposed methodology achieved an average Accuracy of 96.12%, Sensitivity of 96.32%, Specificity of 95.84%, and F−1 score of 0.9609 on MESSI-DOR and IDRiD datasets. Conclusion: These excellent results establish the validity of the proposed methodology for use in DME screening and solidifies the applicability of the HE-CNN classification technique in the domain of biomedical imaging.

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“…As such, it is the goal of this research to analyze for the first time the impacts of geographic variation and ethnicity of patients on DR classification performance using a deep learning ResNet architecture. During this analysis, five publicly available fundus image datasets, namely Kaggle, 2 Messidor [16], E-Optha [17], HRF [18], and IDRID (2018 IEEE ISBI Challenge) [19], were used. Sample fundus images from three datasets are shown in Figure 2.…”

Section: Introductionmentioning

confidence: 99%

Geographic variation and ethnicity in diabetic retinopathy detection via deeplearning

Serener¹,

Serte²

2020

Turk J Elec Eng & Comp Sci

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The prevalence of diabetes is on the rise steadily around the globe. Diabetic retinopathy (DR) is a result of damage to the blood vessels in the retina due to diabetes and its fast treatment is crucial for preventing possible blindness. The diagnosis of DR is done mostly using a comprehensive eye exam, where the eye is dilated for better inspection. Analysis by an ophthalmologist is prone to human error and thus automatic and highly accurate detection of DR is preferred for an earlier and better diagnosis. It is important, however, that automatic detection be accurate for all data collected from patients of different geographic and ethnic backgrounds. In this paper, the automatic detection of DR with a deep learning algorithm is analyzed when geographic and ethnic information of the patients is also integrated into the architecture. It is shown that robust and generalizable DR detection performance is linearly related to the correlation of geographic and ethnic patient information between the training and the testing datasets. The deep learning model created eliminates geographic variation in the detection and works for patients of all ethnicities.

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Indian Diabetic Retinopathy Image Dataset (IDRiD): A Database for Diabetic Retinopathy Screening Research

Cited by 610 publications

References 24 publications

The SUSTech-SYSU dataset for automatically segmenting and classifying corneal ulcers

The SUSTech-SYSU dataset for automatically segmenting and classifying corneal ulcers

DMENet: Diabetic Macular Edema diagnosis using Hierarchical Ensemble of CNNs

Geographic variation and ethnicity in diabetic retinopathy detection via deeplearning

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