Automatic detection and grading of diabetic maculopathy using fundus images

Rajput, G. G.; Reshmi, Bharati M.; Rajesh, I.S.

doi:10.1016/j.procs.2020.03.182

Cited by 7 publications

(2 citation statements)

References 11 publications

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“…A multilabel SVM classifier was further applied to achieve an accuracy of 95.1%, specificity of 86.8%, and sensitivity of 86.1%. Rajput et al [ 16 ] used fundus images to identify macular hard exudates, presenting an automatic approach based on mathematical morphology. By taking the extended minima transform’s complement, the hard exudates were extracted.…”

Section: Introductionmentioning

confidence: 99%

Detection of Diabetic Retinopathy Using Extracted 3D Features from OCT Images

Elgafi

Sharafeldeen

Elnakib

et al. 2022

Sensors

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Diabetic retinopathy (DR) is a major health problem that can lead to vision loss if not treated early. In this study, a three-step system for DR detection utilizing optical coherence tomography (OCT) is presented. First, the proposed system segments the retinal layers from the input OCT images. Second, 3D features are extracted from each retinal layer that include the first-order reflectivity and the 3D thickness of the individual OCT layers. Finally, backpropagation neural networks are used to classify OCT images. Experimental studies on 188 cases confirm the advantages of the proposed system over related methods, achieving an accuracy of 96.81%, using the leave-one-subject-out (LOSO) cross-validation. These outcomes show the potential of the suggested method for DR detection using OCT images.

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Section: Introductionmentioning

confidence: 99%

Detection of Diabetic Retinopathy Using Extracted 3D Features from OCT Images

Elgafi

Sharafeldeen

Elnakib

et al. 2022

Sensors

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“…Finally, SVM was used to make the final diagnosis in which the reported accuracy was 82.35% . In another study, an automatic system based on mathematical morphology has been presented by detecting macular hard exudates in FP 5 . The hard exudates are extracted by taking the complement of the extended minima transform.…”

mentioning

confidence: 99%

Precise higher-order reflectivity and morphology models for early diagnosis of diabetic retinopathy using OCT images

Sharafeldeen

Elsharkawy

Khalifa

et al. 2021

Sci Rep

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This study proposes a novel computer assisted diagnostic (CAD) system for early diagnosis of diabetic retinopathy (DR) using optical coherence tomography (OCT) B-scans. The CAD system is based on fusing novel OCT markers that describe both the morphology/anatomy and the reflectivity of retinal layers to improve DR diagnosis. This system separates retinal layers automatically using a segmentation approach based on an adaptive appearance and their prior shape information. High-order morphological and novel reflectivity markers are extracted from individual segmented layers. Namely, the morphological markers are layer thickness and tortuosity while the reflectivity markers are the 1st-order reflectivity of the layer in addition to local and global high-order reflectivity based on Markov-Gibbs random field (MGRF) and gray-level co-occurrence matrix (GLCM), respectively. The extracted image-derived markers are represented using cumulative distribution function (CDF) descriptors. The constructed CDFs are then described using their statistical measures, i.e., the 10th through 90th percentiles with a 10% increment. For individual layer classification, each extracted descriptor of a given layer is fed to a support vector machine (SVM) classifier with a linear kernel. The results of the four classifiers are then fused using a backpropagation neural network (BNN) to diagnose each retinal layer. For global subject diagnosis, classification outputs (probabilities) of the twelve layers are fused using another BNN to make the final diagnosis of the B-scan. This system is validated and tested on 130 patients, with two scans for both eyes (i.e. 260 OCT images), with a balanced number of normal and DR subjects using different validation metrics: 2-folds, 4-folds, 10-folds, and leave-one-subject-out (LOSO) cross-validation approaches. The performance of the proposed system was evaluated using sensitivity, specificity, F1-score, and accuracy metrics. The system’s performance after the fusion of these different markers showed better performance compared with individual markers and other machine learning fusion methods. Namely, it achieved $$96.15\%$$ 96.15 % , $$99.23\%$$ 99.23 % , $$97.66\%$$ 97.66 % , and $$97.69\%$$ 97.69 % , respectively, using the LOSO cross-validation technique. The reported results, based on the integration of morphology and reflectivity markers and by using state-of-the-art machine learning classifications, demonstrate the ability of the proposed system to diagnose the DR early.

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