Microaneurysms segmentation and diabetic retinopathy detection by learning discriminative representations

Sarhan, Mhd Hasan; Albarqouni, Shadi; Yiğitsoy, Mehmet; Navab, Nassir; Eslami, Abouzar

doi:10.1049/iet-ipr.2019.0804

Cited by 10 publications

(4 citation statements)

References 32 publications

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“…Our manual examination of the fundus image reports that the smallest lesion (microaneurysm) is about 5 × 5 pixels at an image size of 512 × 512. A microaneurysm has a small size which constitutes less than 1% of the fundus image reported by Sarhan et al [ 32 ]. The block size should be small to match the smallest lesion, so, our experiments started testing using R = 2 and a block size 5 × 5 pixels.…”

Section: Methodsmentioning

confidence: 99%

Features extraction using encoded local binary pattern for detection and grading diabetic retinopathy

Berbar

2022

Health Inf Sci Syst

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Introduction Reliable computer diagnosis of diabetic retinopathy (DR) is needed to rescue many with diabetes who may be under threat of blindness. This research aims to detect the presence of diabetic retinopathy in fundus images and grade the disease severity without lesion segmentation. Methods To ensure that the fundus images are in a standard state of brightness, a series of preprocessing steps have been applied to the green channel image using histogram matching and a median filter. Then, contrast-limited adaptive histogram equalisation is performed, followed by the unsharp filter. The preprocessed image is divided into small blocks, and then each block is processed to extract uniform local binary patterns (LBPs) features. The extracted features are encoded, and the feature size is reduced to 3.5 percent of its original size. Classifiers like Support Vector Machine (SVM) and a proposed CNN model were used to classify retinal fundus images. The classification is abnormal or normal and to grade the severity of DR. Results Our feature extraction method was tested on a binary classifier and resulted in an accuracy of 98.37% and 98.84% on the Messidor2 and EyePACS databases, respectively. The proposed system could grade DR severity into three grades (0: no DR, 1: mild DR, and 5: moderate, severe NPDR, and PDR). It obtains an F1-score of 0.9617 and an accuracy of 95.37% on the EyePACS database, and an F1-score of 0.9860 and an accuracy of 97.57% on the Messidor2 database. The resultant values are dependent on the selection of (neighbours, radius) pairs during the extraction of LBP features. Conclusions This study’s results proved that the preprocessing steps are significant and had a great effect on highlighting image features. The novel method of stacking and encoding the LBP values in the feature vector greatly affects results when using SVM or CNN for classification. The proposed system outperforms the state of the artwork. The proposed CNN model performs better than SVM.

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Section: Methodsmentioning

confidence: 99%

Features extraction using encoded local binary pattern for detection and grading diabetic retinopathy

Berbar

2022

Health Inf Sci Syst

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“…The lesions in DR images are important for grading DR tasks. Sarhan [32] designed a two-stage deep-learning method to segment the MA by the multi-scale DR images with selective sampling and the triplet loss. Adem et al [33] detected the exudates in DR images by a CNN model with a circular Hough transform.…”

Section: Introductionmentioning

confidence: 99%

A new retinal OCT‐angiography diabetic retinopathy dataset for segmentation and DR grading

Wang

Dai

et al. 2023

Journal of Biophotonics

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PurposeDiabetic retinopathy (DR) is one of the most common diseases caused by diabetes and can lead to vision loss or even blindness. The wide‐field optical coherence tomography (OCT) angiography is non‐invasive imaging technology and convenient to diagnose DR.MethodsA newly constructed Retinal OCT‐Angiography Diabetic retinopathy (ROAD) dataset is utilized for segmentation and grading tasks. It contains 1200 normal images, 1440 DR images, and 1440 ground truths for DR image segmentation. To handle the problem of grading DR, we propose a novel and effective framework, named projective map attention‐based convolutional neural network (PACNet).ResultsThe experimental results demonstrate the effectiveness of our PACNet. The accuracy of the proposed framework for grading DR is 87.5% on the ROAD dataset.ConclusionsThe information on ROAD can be viewed at URL https://mip2019.github.io/ROAD. The ROAD dataset will be helpful for the development of the early detection of DR field and future research.Translational RelevanceThe novel framework for grading DR is a valuable research and clinical diagnosis method.

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“…Mazlan et al proposed a detection method for using H-maxima and thresholding technique 10 . Sarhan et al proposed a two-stage deep learning approach for MA segmentation using the multiple scales of the input with selective sampling and embedding triplet loss 11 . Kou et al proposed an architecture for U-Net obtained by combining the deep residual model and recurrent convolutional operations into U-Net 12 .…”

Section: Introductionmentioning

confidence: 99%

A new detection model of microaneurysms based on improved FC-DenseNet

Wang

Yao

et al. 2022

Sci Rep

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Diabetic retinopathy (DR) is a frequent vascular complication of diabetes mellitus and remains a leading cause of vision loss worldwide. Microaneurysm (MA) is usually the first symptom of DR that leads to blood leakage in the retina. Periodic detection of MAs will facilitate early detection of DR and reduction of vision injury. In this study, we proposed a novel model for the detection of MAs in fluorescein fundus angiography (FFA) images based on the improved FC-DenseNet, MAs-FC-DenseNet. FFA images were pre-processed by the Histogram Stretching and Gaussian Filtering algorithm to improve the quality of FFA images. Then, MA regions were detected by the improved FC-DenseNet. MAs-FC-DenseNet was compared against other FC-DenseNet models (FC-DenseNet56 and FC-DenseNet67) or the end-to-end models (DeeplabV3+ and PSPNet) to evaluate the detection performance of MAs. The result suggested that MAs-FC-DenseNet had higher values of evaluation metrics than other models, including pixel accuracy (PA), mean pixel accuracy (MPA), precision (Pre), recall (Re), F1-score (F1), and mean intersection over union (MIoU). Moreover, MA detection performance for MAs-FC-DenseNet was very close to the ground truth. Taken together, MAs-FC-DenseNet is a reliable model for rapid and accurate detection of MAs, which would be used for mass screening of DR patients.

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Microaneurysms segmentation and diabetic retinopathy detection by learning discriminative representations

Cited by 10 publications

References 32 publications

Features extraction using encoded local binary pattern for detection and grading diabetic retinopathy

Features extraction using encoded local binary pattern for detection and grading diabetic retinopathy

A new retinal OCT‐angiography diabetic retinopathy dataset for segmentation and DR grading

A new detection model of microaneurysms based on improved FC-DenseNet

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