Deep tessellated retinal image detection using Convolutional Neural Networks

Lyu, Xingzheng; Li, Hai; Zhen, Yi; Ji, Xin; Zhang, Sanyuan

doi:10.1109/embc.2017.8036915

Cited by 12 publications

(9 citation statements)

References 18 publications

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“…The result,

, can be seen in Figure 4 b. There is another type of structure that is easily distinguishable in

: the underlying choroidal vasculature visible in tigroid retinas [ 45 ]. This is caused by the lack of pigments in the retinal pigment epithelium and is common in aged or myopic patients [ 45 ].…”

Section: Methodsmentioning

confidence: 99%

“…There is another type of structure that is easily distinguishable in

and can hinder the detection of RLs due to the high contrast it shows against the background.…”

Section: Methodsmentioning

confidence: 99%

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Effective Fundus Image Decomposition for the Detection of Red Lesions and Hard Exudates to Aid in the Diagnosis of Diabetic Retinopathy

Romero-Oraá

García

Oraá-Pérez

et al. 2020

Sensors

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Diabetic retinopathy (DR) is characterized by the presence of red lesions (RLs), such as microaneurysms and hemorrhages, and bright lesions, such as exudates (EXs). Early DR diagnosis is paramount to prevent serious sight damage. Computer-assisted diagnostic systems are based on the detection of those lesions through the analysis of fundus images. In this paper, a novel method is proposed for the automatic detection of RLs and EXs. As the main contribution, the fundus image was decomposed into various layers, including the lesion candidates, the reflective features of the retina, and the choroidal vasculature visible in tigroid retinas. We used a proprietary database containing 564 images, randomly divided into a training set and a test set, and the public database DiaretDB1 to verify the robustness of the algorithm. Lesion detection results were computed per pixel and per image. Using the proprietary database, 88.34% per-image accuracy (ACCi), 91.07% per-pixel positive predictive value (PPVp), and 85.25% per-pixel sensitivity (SEp) were reached for the detection of RLs. Using the public database, 90.16% ACCi, 96.26% PPV_p, and 84.79% SEp were obtained. As for the detection of EXs, 95.41% ACCi, 96.01% PPV_p, and 89.42% SE_p were reached with the proprietary database. Using the public database, 91.80% ACCi, 98.59% PPVp, and 91.65% SEp were obtained. The proposed method could be useful to aid in the diagnosis of DR, reducing the workload of specialists and improving the attention to diabetic patients.

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“…The result,

, can be seen in Figure 4 b. There is another type of structure that is easily distinguishable in

Section: Methodsmentioning

confidence: 99%

“…There is another type of structure that is easily distinguishable in

and can hinder the detection of RLs due to the high contrast it shows against the background.…”

Section: Methodsmentioning

confidence: 99%

Effective Fundus Image Decomposition for the Detection of Red Lesions and Hard Exudates to Aid in the Diagnosis of Diabetic Retinopathy

Romero-Oraá

García

Oraá-Pérez

et al. 2020

Sensors

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show abstract

“…The computational time analysis is conducted for the proposed methodology by comparing it with the existing techniques and the results obtained are presented in Table 5. The state-of-art techniques taken for comparison are CNN [17], TorchIO [24], DenseCapsNet [25], HLSE [28], and HDWE [29]. The computational time of the proposed Faster R-CNN with WGA is 96.3 s which is relatively lower than the existing techniques (CNN(259.45 s), TorchIO (159.359 s), DenseCapsNet (154.55 s), HLSE (126.5 s), and HDWE (188.25 s)).…”

Section: Classification Resultsmentioning

confidence: 99%

“…According to the INSPIRE dataset, the classification results of 91.3% specificity, 89.6% sensitivity, and 90.2% accuracy were attained but the larger feature set made computational difficulties. Convolutional Neural Networks (CNN) was suggested by Lyu et al [17] for deep tessellated retinal image detection. The classification performances were evaluated by using 12,000 color retinal images based on the database.…”

Section: Review Of Related Workmentioning

confidence: 99%

Multithreshold Image Segmentation Technique Using Remora Optimization Algorithm for Diabetic Retinopathy Detection from Fundus Images

Vinayaki

Kalaiselvi

2022

Neural Process Lett

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One of the most common complications of diabetes mellitus is diabetic retinopathy (DR), which produces lesions on the retina. A novel framework for DR detection and classification was proposed in this study. The proposed work includes four stages: pre-processing, segmentation, feature extraction, and classification. Initially, the image pre-processing is performed and after that, the Multi threshold-based Remora Optimization (MTRO) algorithm performs the vessel segmentation. The feature extraction and classification process are done by using a Region-based Convolution Neural Network (R-CNN) with Wild Geese Algorithm (WGA). Finally, the proposed R-CNN with WGA effectively classifies the different stages of DR including Non-DR, Proliferative DR, Severe, Moderate DR, Mild DR. The experimental images were collected from the DRIVE database, and the proposed framework exhibited superior DR detection performance. Compared to other existing methods like fully convolutional deep neural network (FCDNN), genetic-search feature selection (GSFS), Convolutional Neural Networks (CNN), and deep learning (DL) techniques, the proposed R-CNN with WGA provided 95.42% accuracy, 93.10% specificity, 93.20% sensitivity, and 98.28% F-score results.

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“…Lyu et al . [ 28 ] constructed three convolutional neural network models to identify healthy and TF fundi, with the results indicating effective TF fundus detection performance. Chunsheng et al .…”

mentioning

confidence: 99%

Research on an artificial intelligence-based myopic maculopathy grading method using EfficientNet

Zheng,

Zhang,

Zhu

et al. 2023

Indian Journal of Ophthalmology

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Purpose: We aimed to develop an artificial intelligence-based myopic maculopathy grading method using EfficientNet to overcome the delayed grading and diagnosis of different myopic maculopathy degrees. Methods: The cooperative hospital provided 4642 healthy and myopic maculopathy color fundus photographs, comprising the four degrees of myopic maculopathy and healthy fundi. The myopic maculopathy grading models were trained using EfficientNet-B0 to EfficientNet-B7 models. The diagnostic results were compared with those of the VGG16 and ResNet50 classification models. The leading evaluation indicators were sensitivity, specificity, F1 score, area under the receiver operating characteristic (ROC) curve area under curve (AUC), 95% confidence interval, kappa value, and accuracy. The ROC curves of the ten grading models were also compared. Results: We used 1199 color fundus photographs to evaluate the myopic maculopathy grading models. The size of the EfficientNet-B0 myopic maculopathy grading model was 15.6 MB, and it had the highest kappa value (88.32%) and accuracy (83.58%). The model's sensitivities to diagnose tessellated fundus (TF), diffuse chorioretinal atrophy (DCA), patchy chorioretinal atrophy (PCA), and macular atrophy (MA) were 96.86%, 75.98%, 64.67%, and 88.75%, respectively. The specificity was above 93%, and the AUCs were 0.992, 0.960, 0.964, and 0.989, respectively. Conclusion: The EfficientNet models were used to design grading diagnostic models for myopic maculopathy. Based on the collected fundus images, the models could diagnose a healthy fundus and four types of myopic maculopathy. The models might help ophthalmologists to make preliminary diagnoses of different degrees of myopic maculopathy.

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Deep tessellated retinal image detection using Convolutional Neural Networks

Cited by 12 publications

References 18 publications

Effective Fundus Image Decomposition for the Detection of Red Lesions and Hard Exudates to Aid in the Diagnosis of Diabetic Retinopathy

Effective Fundus Image Decomposition for the Detection of Red Lesions and Hard Exudates to Aid in the Diagnosis of Diabetic Retinopathy

Multithreshold Image Segmentation Technique Using Remora Optimization Algorithm for Diabetic Retinopathy Detection from Fundus Images

Research on an artificial intelligence-based myopic maculopathy grading method using EfficientNet

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