scholarly journals Segmenting the Optic Disc Using a Deep Learning Ensemble Model Based on OWA Operators

2021 ◽  
Author(s):  
Mohammed Yousef Salem Ali ◽  
Mohamed Abdel-Nasser ◽  
Mohammed Jabreel ◽  
Aida Valls ◽  
Marc Baget
Keyword(s):  
Deep Learning ◽  
Optic Disc ◽  
Vision Loss ◽  
Semantic Segmentation ◽  
Medical Image Segmentation ◽  
Weighted Averaging ◽  
Fundus Images ◽  
Owa Operators ◽  
Detection Systems ◽  
Glaucoma Detection

The optic disc (OD) is the point where the retinal vessels begin. OD carries essential information linked to Diabetic Retinopathy and glaucoma that may cause vision loss. Therefore, accurate segmentation of the optic disc from eye fundus images is essential to develop efficient automated DR and glaucoma detection systems. This paper presents a deep learning-based system for OD segmentation based on an ensemble of efficient semantic segmentation models for medical image segmentation. The aggregation of the different DL models was performed with the ordered weighted averaging (OWA) operators. We proposed the use of a dynamically generated set of weights that can give a different contribution to the models according to their performance during the segmentation of OD in the eye fundus images. The effectiveness of the proposed system was assessed on a fundus image dataset collected from the Hospital Sant Joan de Reus. We obtained Jaccard, Dice, Precision, and Recall scores of 95.40, 95.10, 96.70, and 93.90%, respectively.

scholarly journals Deep learning based approach for optic disc and optic cup semantic segmentation for glaucoma analysis in retinal fundus images

2020 ◽  
Vol 11 (2) ◽  
pp. 111-120
Author(s):  
Dunja Božić-Štulić ◽  
Maja Braović ◽  
Darko Stipaničev
Keyword(s):  
Deep Learning ◽  
Optic Disc ◽  
Contextual Information ◽  
Automatic Detection ◽  
Semantic Segmentation ◽  
Fundus Images ◽  
Glaucoma Detection ◽  
Average Accuracy ◽  
Optic Cup ◽  
Retinal Fundus

Optic disc and optic cup are one of the most recognized retinal landmarks, and there are numerous methods for their automatic detection. Segmented optic disc and optic cup are useful in providing the contextual information about the retinal image that can aid in the detection of other retinal features, but it is also useful in the automatic detection and monitoring of glaucoma. This paper proposes a deep learning based approach for the automatic optic disc and optic cup semantic segmentation, but also the new model for possible glaucoma detection. The proposed method was trained on DRIVE and DIARETDB1 image datasets and evaluated on MESSIDOR dataset, where it achieved the average accuracy of 97.3% of optic disc and 88.1% of optic cup. Detection rate of glaucoma diesis is 96.75%

scholarly journals An Efficient Deep Learning Approach to Automatic Glaucoma Detection Using Optic Disc and Optic Cup Localization

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 434
Author(s):  
Marriam Nawaz ◽  
Tahira Nazir ◽  
Ali Javed ◽  
Usman Tariq ◽  
Hwan-Seung Yong ◽  
...  
Keyword(s):  
Deep Learning ◽  
Optic Disc ◽  
Vision Loss ◽  
Image Database ◽  
Eye Color ◽  
Glaucoma Screening ◽  
Glaucoma Detection ◽  
Retinal Fundus Image ◽  
Optic Cup ◽  
Screening Procedures

Glaucoma is an eye disease initiated due to excessive intraocular pressure inside it and caused complete sightlessness at its progressed stage. Whereas timely glaucoma screening-based treatment can save the patient from complete vision loss. Accurate screening procedures are dependent on the availability of human experts who performs the manual analysis of retinal samples to identify the glaucomatous-affected regions. However, due to complex glaucoma screening procedures and shortage of human resources, we often face delays which can increase the vision loss ratio around the globe. To cope with the challenges of manual systems, there is an urgent demand for designing an effective automated framework that can accurately identify the Optic Disc (OD) and Optic Cup (OC) lesions at the earliest stage. Efficient and effective identification and classification of glaucomatous regions is a complicated job due to the wide variations in the mass, shade, orientation, and shapes of lesions. Furthermore, the extensive similarity between the lesion and eye color further complicates the classification process. To overcome the aforementioned challenges, we have presented a Deep Learning (DL)-based approach namely EfficientDet-D0 with EfficientNet-B0 as the backbone. The presented framework comprises three steps for glaucoma localization and classification. Initially, the deep features from the suspected samples are computed with the EfficientNet-B0 feature extractor. Then, the Bi-directional Feature Pyramid Network (BiFPN) module of EfficientDet-D0 takes the computed features from the EfficientNet-B0 and performs the top-down and bottom-up keypoints fusion several times. In the last step, the resultant localized area containing glaucoma lesion with associated class is predicted. We have confirmed the robustness of our work by evaluating it on a challenging dataset namely an online retinal fundus image database for glaucoma analysis (ORIGA). Furthermore, we have performed cross-dataset validation on the High-Resolution Fundus (HRF), and Retinal Image database for Optic Nerve Evaluation (RIM ONE DL) datasets to show the generalization ability of our work. Both the numeric and visual evaluations confirm that EfficientDet-D0 outperforms the newest frameworks and is more proficient in glaucoma classification.

scholarly journals Joint Optic Disc and Cup Segmentation Based on Densely Connected Depthwise Separable Convolution Deep Network

2020 ◽  
Author(s):  
Bingyan Liu ◽  
Daru Pan ◽  
Hui Song
Keyword(s):  
Deep Learning ◽  
Optic Disc ◽  
Vision Loss ◽  
State Of The Art ◽  
Semantic Segmentation ◽  
Important Indicator ◽  
Optic Cup ◽  
Segmentation Task ◽  
Screening And Diagnosis ◽  
Cup To Disc Ratio

Abstract Background: Glaucoma is an eye disease that causes vision loss and even blindness. The cup to disc ratio (CDR) is an important indicator for glaucoma screening and diagnosis. Accurate segmentation for the optic disc and cup helps obtain CDR. Although many deep learning-based methods have been proposed to segment the disc and cup for fundus image, achieving highly accurate segmentation performance is still a great challenge due to the heavy overlap between the optic disc and cup.Methods: In this paper, we propose a two-stage method where the optic disc is firstly located and then the optic disc and cup are segmented jointly according to the interesting areas. Also, we consider the joint optic disc and cup segmentation task as a multi-category semantic segmentation task for which a deep learning-based model named DDSC-Net (densely connected depthwise separable convolution network) is proposed. Specifically, we employ depthwise separable convolutional layer and image pyramid input to form a deeper and wider networkto improve segmentation performance. Finally, we evaluate our method on two publicly available datasets, Drishti-GS and REFUGE dataset.Results: The experiment results show that the proposed method outperforms state-of-the-art methods, such as pOSAL, GL-Net, M-Net and Stack-U-Net in terms of disc coefficients, with the scores of 0.9780 (optic disc) and 0.9123 (optic cup) on the DRISHTI-GS dataset, and the scores of 0.9601 (optic disc) and 0.8903 (optic cup) on the REFUGE dataset. Particularly, in the more challenging optic cup segmentation task, our method outperforms GL-Net by 0.7 % in terms of disc coefficients on the Drishti-GS dataset and outperforms pOSAL by 0.79 %on the REFUGE dataset, respectively.Conclusions: The promising segmentation performances reveal that our method has the potential in assisting the screening and diagnosis of glaucoma.

scholarly journals Image-based Glaucoma Classification Using Fundus Images and Deep Learning

2021 ◽  
Author(s):  
◽  
Hiram José Sandoval-Cuellar
Keyword(s):  
Deep Learning ◽  
Vision Loss ◽  
Rapid Development ◽  
Area Under The Curve ◽  
Region Of Interest ◽  
Fundus Images ◽  
Non Invasive ◽  
Glaucoma Detection ◽  
Initial Stage ◽  
Interest Detection

Glaucoma is an eye disease that gradually affects the optic nerve. Intravascular high pressure can be controlled to prevent total vision loss, but early glaucoma detection is crucial. The optic disc has been a notable landmark for finding abnormalities in the retina. The rapid development of computer vision techniques has made it possible to analyze eye conditions from images enabling to help a specialist to make a diagnosis using a technique that is non-invasive in its initial stage through fundus images. We propose a methodology glaucoma detection using deep learning. A convolutional neural network (CNN) is trained to extract multiple features, to classify fundus images. The accuracy, sensitivity, and the area under the curve obtained using the ORIGA database are 93.22%, 94.14%, and 93.98%. The use of the algorithm for the automatic region of interest detection in conjunction with our CNN structure considerably increases the glaucoma detecting accuracy in the ORIGA database.

scholarly journals Joint optic disc and cup segmentation based on densely connected depthwise separable convolution deep network

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Bingyan Liu ◽  
Daru Pan ◽  
Hui Song
Keyword(s):  
Deep Learning ◽  
Optic Disc ◽  
Vision Loss ◽  
State Of The Art ◽  
Semantic Segmentation ◽  
Important Indicator ◽  
Optic Cup ◽  
Segmentation Task ◽  
Screening And Diagnosis ◽  
Cup To Disc Ratio

Abstract Background Glaucoma is an eye disease that causes vision loss and even blindness. The cup to disc ratio (CDR) is an important indicator for glaucoma screening and diagnosis. Accurate segmentation for the optic disc and cup helps obtain CDR. Although many deep learning-based methods have been proposed to segment the disc and cup for fundus image, achieving highly accurate segmentation performance is still a great challenge due to the heavy overlap between the optic disc and cup. Methods In this paper, we propose a two-stage method where the optic disc is firstly located and then the optic disc and cup are segmented jointly according to the interesting areas. Also, we consider the joint optic disc and cup segmentation task as a multi-category semantic segmentation task for which a deep learning-based model named DDSC-Net (densely connected depthwise separable convolution network) is proposed. Specifically, we employ depthwise separable convolutional layer and image pyramid input to form a deeper and wider network to improve segmentation performance. Finally, we evaluate our method on two publicly available datasets, Drishti-GS and REFUGE dataset. Results The experiment results show that the proposed method outperforms state-of-the-art methods, such as pOSAL, GL-Net, M-Net and Stack-U-Net in terms of disc coefficients, with the scores of 0.9780 (optic disc) and 0.9123 (optic cup) on the DRISHTI-GS dataset, and the scores of 0.9601 (optic disc) and 0.8903 (optic cup) on the REFUGE dataset. Particularly, in the more challenging optic cup segmentation task, our method outperforms GL-Net by 0.7$$\%$$ % in terms of disc coefficients on the Drishti-GS dataset and outperforms pOSAL by 0.79$$\%$$ % on the REFUGE dataset, respectively. Conclusions The promising segmentation performances reveal that our method has the potential in assisting the screening and diagnosis of glaucoma.

scholarly journals Joint Optic Disc and Cup Segmentation Based on Densely Connected Depthwise Separable Convolution Deep Network

2020 ◽  
Author(s):  
Bingyan Liu ◽  
Daru Pan ◽  
Hui Song
Keyword(s):  
Deep Learning ◽  
Optic Disc ◽  
Vision Loss ◽  
State Of The Art ◽  
Semantic Segmentation ◽  
Important Indicator ◽  
Optic Cup ◽  
Segmentation Task ◽  
Screening And Diagnosis ◽  
Cup To Disc Ratio

Abstract Background: Glaucoma is an eye disease that causes vision loss and even blindness. The cup to disc ratio (CDR) is an important indicator for glaucoma screening and diagnosis. Accurate segmentation for the optic disc and cup helps obtain CDR. Although many deep learning-based methods have been proposed to segment the disc and cup for fundus image, achieving highly accurate segmentation performance is still a great challenge due to the heavy overlap between the optic disc and cup. Methods: In this paper, we propose a two-stage method where the optic disc is firstly located and then the optic disc and cup are segmented jointly according to the interesting areas. Also, we consider the joint optic disc and cup segmentation task as a multi-category semantic segmentation task for which a deep learning-based model named DDSC-Net (densely connected depthwise separable convolution network) is proposed. Specifically, we employ depthwise separable convolutional layer and image pyramid input to form a deeper and wider network to improve segmentation performance. Finally, we evaluate our method on two publicly available datasets, Drishti-GS and REFUGE dataset. Results: The experiment results show that the proposed method outperforms state-of-the-art methods, such as pOSAL, GL-Net, M-Net and Stack-U-Net in terms of disc coefficients, with the scores of 0.9780 (optic disc) and 0.9123 (optic cup) on the DRISHTI-GS dataset, and the scores of 0.9601 (optic disc) and 0.8903 (optic cup) on the REFUGE dataset. Particularly, in the more challenging optic cup segmentation task, our method outperforms GL-Net by 0.7 % in terms of disc coefficients on the Drishti-GS dataset and outperforms pOSAL by 0.79 % on the REFUGE dataset, respectively. Conclusions: The promising segmentation performances reveal that our method has the potential in assisting the screening and diagnosis of glaucoma.

An Explainable Deep Learning Ensemble Model for Robust Diagnosis of Diabetic Retinopathy Grading

2021 ◽  
Vol 17 (3s) ◽  
pp. 1-24
Author(s):  
Mohammad Shorfuzzaman ◽  
M. Shamim Hossain ◽  
Abdulmotaleb El Saddik
Keyword(s):  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Vision Loss ◽  
Final Diagnosis ◽  
Learning Rate ◽  
Ensemble Model ◽  
Fundus Images ◽  
Robust Detection ◽  
Retinal Fundus Images ◽  
Retinal Fundus

Diabetic retinopathy (DR) is one of the most common causes of vision loss in people who have diabetes for a prolonged period. Convolutional neural networks (CNNs) have become increasingly popular for computer-aided DR diagnosis using retinal fundus images. While these CNNs are highly reliable, their lack of sufficient explainability prevents them from being widely used in medical practice. In this article, we propose a novel explainable deep learning ensemble model where weights from different models are fused into a single model to extract salient features from various retinal lesions found on fundus images. The extracted features are then fed to a custom classifier for the final diagnosis of DR severity level. The model is trained on an APTOS dataset containing retinal fundus images of various DR grades using a cyclical learning rates strategy with an automatic learning rate finder for decaying the learning rate to improve model accuracy. We develop an explainability approach by leveraging gradient-weighted class activation mapping and shapely adaptive explanations to highlight the areas of fundus images that are most indicative of different DR stages. This allows ophthalmologists to view our model's decision in a way that they can understand. Evaluation results using three different datasets (APTOS, MESSIDOR, IDRiD) show the effectiveness of our model, achieving superior classification rates with a high degree of precision (0.970), sensitivity (0.980), and AUC (0.978). We believe that the proposed model, which jointly offers state-of-the-art diagnosis performance and explainability, will address the black-box nature of deep CNN models in robust detection of DR grading.

scholarly journals Vision Atrophy Screening and Revelation

2021 ◽  
Author(s):  
Kiruthiga Devi M ◽  
Lingamuthu K ◽  
Baskar M ◽  
Deepa B ◽  
Merlin G
Keyword(s):  
Optic Disc ◽  
Vision Loss ◽  
Low Cost ◽  
Remote Diagnostics ◽  
Glaucoma Detection ◽  
Screening Services ◽  
Detection And Diagnosis ◽  
Retinal Fundus ◽  
To Receive

Glaucoma which is known as the “thief of sight”, is an irreversible eye disease It is mainly caused by increased intraocular pressure (IOP), or loss of blood supply to the optic nerve. Glaucoma detection and diagnosis is very important. By analyzing the optic disc and its surroundings, This paper introduces a method for providing automated glaucoma screening services based on a framework that proposes a retinal image synthesizer for glaucoma assessment by analyzing the optic disc and its surroundings. The Cup to Disc Ratio (CDR) is critical for the system, and it is calculated using 2-D retinal fundus images. The synthetic images produced by our system are compared quantitatively. The structural properties of synthetic and real images are analyzed, and the quality of colour is calculated by extracting the 2-D histogram. The system allows patients to receive low-cost remote diagnostics from a distance, preventing blindness and vision loss by early detection and management.

scholarly journals In Search for the Optimal Preprocessing Technique for Deep Learning Based Diabetic Retinopathy Stage Classification from Fundus Images

2021 ◽  
Author(s):  
Nilarun Mukherjee ◽  
Souvik Sengupta
Keyword(s):  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Vision Loss ◽  
Fundus Images ◽  
New Combinations ◽  
Z Score ◽  
Kappa Score ◽  
Stage Classification ◽  
Classification Tasks ◽  
Intensity Normalization

Abstract Background: Diabetic retinopathy (DR) is a complication of diabetes mellitus, which if left untreated may lead to complete vision loss. Early diagnosis and treatment is the key to prevent further complications of DR. Computer-aided diagnosis is a very effective method to support ophthalmologists, as manual inspection of pathological changes in retina images are time consuming and expensive. In recent times, Machine Learning and Deep Learning techniques have subsided conventional rule based approaches for detection, segmentation and classification of DR stages and lesions in fundus images. Method: In this paper, we present a comparative study of the different state-of-the-art preprocessing methods that have been used in deep learning based DR classification tasks in recent times and also propose a new unsupervised learning based retinal region extraction technique and new combinations of preprocessing pipelines designed on top of it. Efficacy of different existing and new combinations of the preprocessing methods are analyzed using two publicly available retinal datasets (EyePACS and APTOS) for different DR stage classification tasks, such as referable DR, DR screening, and five-class DR grading, using a benchmark deep learning model (ResNet-50). Results: It has been observed that the proposed preprocessing strategy composed of region of interest extraction through K-means clustering followed by contrast and edge enhancement using Graham’s method and z-score intensity normalization achieved the highest accuracy of 98.5%, 96.51% and 90.59% in DR-screening, referable-DR, and DR gradation tasks respectively and also achieved the best quadratic weighted kappa score of 0.945 in DR grading task. It achieved best AUC-ROC of 0.98 and 0.9981 in DR grading and DR screening tasks respectively. Conclusion: It is evident from the results that the proposed preprocessing pipeline composed of the proposed ROI extraction through K-means clustering, followed by edge and contrast enhancement using Graham’s method and then z-score intensity normalization outperforms all other existing preprocessing pipelines and has proven to be the most effective preprocessing strategy in helping the baseline CNN model to extract meaningful deep features.

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