Diabetic Retinopathy Grading by Digital Curvelet Transform

Alipour, Shirin Hajeb Mohammad; Rabbani, Hossein; Akhlaghi, Mohammadreza

doi:10.1155/2012/761901

Cited by 80 publications

(45 citation statements)

References 23 publications

(32 reference statements)

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“…The Fundus-FFA dataset [9] contains color fundus images with the corresponding FFA images, which is not pixelaligned. Based on this consideration, we trained a CycleGAN model [8] and we followed the original setting to train the network with both adversarial loss and cycle-consistency loss.…”

Section: B Ffa Image Synthesizationmentioning

confidence: 99%

Self-Supervised Feature Learning via Exploiting Multi-Modal Data for Retinal Disease Diagnosis

Jia

Islam

et al. 2020

IEEE Trans. Med. Imaging

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The automatic diagnosis of various retinal diseases from fundus images is important to support clinical decisionmaking. However, developing such automatic solutions is challenging due to the requirement of a large amount of humanannotated data. Recently, unsupervised/self-supervised feature learning techniques receive a lot of attention, as they do not need massive annotations. Most of the current self-supervised methods are analyzed with single imaging modality and there is no method currently utilize multi-modal images for better results. Considering that the diagnostics of various vitreoretinal diseases can greatly benefit from another imaging modality, e.g., FFA, this paper presents a novel self-supervised feature learning method by effectively exploiting multi-modal data for retinal disease diagnosis. To achieve this, we first synthesize the corresponding FFA modality and then formulate a patient feature-based softmax embedding objective. Our objective learns both modality-invariant features and patient-similarity features. Through this mechanism, the neural network captures the semantically shared information across different modalities and the apparent visual similarity between patients. We evaluate our method on two public benchmark datasets for retinal disease diagnosis. The experimental results demonstrate that our method clearly outperforms other self-supervised feature learning methods and is comparable to the supervised baseline. Our code is available at GitHub 1. Index Terms-Retinal disease diagnosis, self-supervised learning, multi-modal data I. INTRODUCTION C OLOR fundus photography has been widely used in clinical practice to evaluate various conventional ophthalmic diseases, e.g., age-related macular degeneration (AMD) [1], pathologic myopia (PM) [2], and diabetic retinopathy [3, 4]. Recently, deep learning has shown very good performance on a variety of automatic ophthalmic disease detection problems from fundus images [5-7], and these techniques can help ophthalmologists in decision making. The success is attributed to the learned representative features from fundus images, which requires a large amount of training data with massive human annotations. However, it is tedious and expensive to annotate the fundus images, since experts are needed to provide reliable labels. Hence, in this paper, our goal is to learn the representative features from data itself, without any human

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Section: B Ffa Image Synthesizationmentioning

confidence: 99%

Self-Supervised Feature Learning via Exploiting Multi-Modal Data for Retinal Disease Diagnosis

Jia

Islam

et al. 2020

IEEE Trans. Med. Imaging

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“…In diagnosing the DR signs, Hajeb et al [16] employed the CLAHE method to enhance the image, applied curvelet transform to extract features from images, that finally fed the images into a Support Vector Machine (SVM). The CLAHE method performs better than other contrast enhancement methods to augment the vessels in the retinal fundus images with different backgrounds [17].…”

Section: Related Workmentioning

confidence: 99%

Automatic Detection and Monitoring of Diabetic Retinopathy Using Efficient Convolutional Neural Networks and Contrast Limited Adaptive Histogram Equalization

et al. 2020

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Diabetic retinopathy is a medical condition of the damaged retina that is caused by diabetes and lack of proper monitoring and treatment, which usually leads to blindness. However, diabetic retinopathy monitoring requires an expert ophthalmologist. Recently, automatic monitoring models with acceptable efficiency are suggested as an alternative for expert ophthalmologists. In this paper, a new diabetic retinopathy monitoring model is proposed by using the Contrast Limited Adaptive Histogram Equalization method to improve the image quality and equalize intensities uniformly as the pre-processing step. Then, EfficientNet-B5 architecture is used for the classification step. The efficiency of this network is in uniformly scaling all dimensions of the network. The final model is trained once on a mixture of two datasets, Messidor-2 and IDRiD, and evaluated on the Messidor dataset. The area under the curve (AUC) is enhanced from 0.936, which is the highest value in all recent works, to 0.945. Also, once again, to further evaluate the performance of the model, it is trained on a mixture of two datasets, Messidor-2 and Messidor, and evaluated on the IDRiD dataset. In this case, the AUC is enhanced from 0.796, which is the highest value in all recent works, to 0.932. In comparison to other studies, our proposed model improves the AUC.

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“…Healthy and ME fundus scans in the Rabbani Dataset [39,40], which are processed by the proposed system. ( a ) Original healthy fundus scans; ( b ) original ME fundus scans; ( c ) classified as healthy by the proposed system and ( d ) classified as ME by the proposed system.…”

Section: Figurementioning

confidence: 99%

Deep Ensemble Learning Based Objective Grading of Macular Edema by Extracting Clinically Significant Findings from Fused Retinal Imaging Modalities

Hassan

et al. 2019

Sensors

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Macular edema (ME) is a retinal condition in which central vision of a patient is affected. ME leads to accumulation of fluid in the surrounding macular region resulting in a swollen macula. Optical coherence tomography (OCT) and the fundus photography are the two widely used retinal examination techniques that can effectively detect ME. Many researchers have utilized retinal fundus and OCT imaging for detecting ME. However, to the best of our knowledge, no work is found in the literature that fuses the findings from both retinal imaging modalities for the effective and more reliable diagnosis of ME. In this paper, we proposed an automated framework for the classification of ME and healthy eyes using retinal fundus and OCT scans. The proposed framework is based on deep ensemble learning where the input fundus and OCT scans are recognized through the deep convolutional neural network (CNN) and are processed accordingly. The processed scans are further passed to the second layer of the deep CNN model, which extracts the required feature descriptors from both images. The extracted descriptors are then concatenated together and are passed to the supervised hybrid classifier made through the ensemble of the artificial neural networks, support vector machines and naïve Bayes. The proposed framework has been trained on 73,791 retinal scans and is validated on 5100 scans of publicly available Zhang dataset and Rabbani dataset. The proposed framework achieved the accuracy of 94.33% for diagnosing ME and healthy subjects and achieved the mean dice coefficient of 0.9019 ± 0.04 for accurately extracting the retinal fluids, 0.7069 ± 0.11 for accurately extracting hard exudates and 0.8203 ± 0.03 for accurately extracting retinal blood vessels against the clinical markings.

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Diabetic Retinopathy Grading by Digital Curvelet Transform

Cited by 80 publications

References 23 publications

Self-Supervised Feature Learning via Exploiting Multi-Modal Data for Retinal Disease Diagnosis

Self-Supervised Feature Learning via Exploiting Multi-Modal Data for Retinal Disease Diagnosis

Automatic Detection and Monitoring of Diabetic Retinopathy Using Efficient Convolutional Neural Networks and Contrast Limited Adaptive Histogram Equalization

Deep Ensemble Learning Based Objective Grading of Macular Edema by Extracting Clinically Significant Findings from Fused Retinal Imaging Modalities

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