Survey on recent developments in automatic detection of diabetic retinopathy

Alsallakh, Bilal; Sun, Guangmin; Mazhar, Sarah

doi:10.1016/j.jfo.2020.08.009

Cited by 45 publications

(17 citation statements)

References 78 publications

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“…Microaneurysm (MA) is recognized as the first symptom of DR that leads to retinal blood injury. Detection of MAs within FFA images facilitates early DR detection and prevents vision loss 30 . However, MA is extremely small and its contrast to the surrounding background is very subtle, which make MA detection challenging.…”

Section: Discussionmentioning

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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Section: Discussionmentioning

confidence: 99%

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

Wang

Yao

et al. 2022

Sci Rep

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“…Microaneurysms are the first clinically observable manifestations of diabetic retinopathy. Early diagnosis and timely intervention can halt or reverse the progression of this disease [22]. DenseNet.…”

Section: Discussionmentioning

confidence: 99%

An Automatic Detection Model of Microaneurysms Based on Improved FC-Densenet

Wang¹,

Yao²,

Li³

et al. 2021

Preprint

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Diabetic retinopathy (DR) is a common eye disease, which leads cause of blindness all around the world. Microaneurysms (MAs) is one of the early symptoms of DR. Accurate and effective MAs detection and segmentation is an important step for the diagnosis and treatment of DR. In this paper, we propose an automatic model for detection of MAs in fluorescein fundus angiography (FFA) images. The model mainly consists of two steps. The first step is pre-processing of FFA images, where the quality of FFA images is improved by Histogram Stretching and Gaussian Filtering algorithm. The second step is to detect MAs regions, where the MAs regions are detected by improved FC-DenseNet. We compare the proposed model with traditional FC-DenseNet model and other previously published models. The experimental result shows that our proposed model has the highest scores on evaluation metrics of pixel accuracy (PA), mean pixel accuracy (MPA), precision (Pre), recall (Re), F1-score (F1) and mean intersection over union (MIoU), which are 99.97%, 94.19%, 88.40%, 89.70%, 88.98% and 90.14%, respectively. The result suggests that the performance of our proposed model is closer to the ground truth of MAs detection. Our proposed model would be helpful for ophthalmologists to find the symptoms more quickly and to take better treatment measures in the screening process of diabetic retinopathy.

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“…In this case, early diagnosis of DR can facilitate the patients to have regular treatment with utmost care. As per the survey study, 6 many medical experts have stated that early detection of DR will save nearly 90% of diabetic patients. The identification of DR can be made by the physician either manually or with machine learning or deep learning framework‐based diagnostic system.…”

Section: Introductionmentioning

confidence: 94%

“…F1 score = 2 × (Recall × Precision) Recall + Precision . (6) Area under the curve (AUC): AUC is a graphical representation that shows the relationship between sensitivity and specificity.…”

Section: Autoencodermentioning

confidence: 99%

Detection of diabetic retinopathy and related retinal disorders using fundus images based on deep learning and image processing techniques: A comprehensive review

Lalithadevi

Krishnaveni²

2022

Concurrency and Computation

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Diabetes mellitus is a chronic disorder disease in which a person's body fails to adhere insulin produced by their pancreas or unable to segregate enough insulin due to harmonic imbalance. Diabetic people are suffering from eye disorders like diabetic retinopathy (DR), glaucoma and various diseases such as neuropathy, nephropathy, cardiomyopathy over long intervals. One of the most prevalent diabetic consequence is DR. Detecting the morphological variations in retina is difficult and requires an effective automated detection system. DR can be predicted in earlier stage using tremendous development of deep learning models and image processing techniques. Recently, many research articles have been published in DR diagnosis system. This article shows a comprehensive review of automated diagnostic methods for DR detection and other related eye disorders from several points: Causes for DR, publicly available datasets, image preprocessing, segmentation of various DR lesions, feature optimization, various deep learning models, and open research challenges. The study offers a thorough overview of DR detection techniques, which delivers valuable information for researchers, medical professionals, and DR affected patients.

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Survey on recent developments in automatic detection of diabetic retinopathy

Cited by 45 publications

References 78 publications

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

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

An Automatic Detection Model of Microaneurysms Based on Improved FC-Densenet

Detection of diabetic retinopathy and related retinal disorders using fundus images based on deep learning and image processing techniques: A comprehensive review

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