Glaucoma screening pipeline based on clinical measurements and hidden features

Zhao, Xin; Guo, Fan; Mai, Yuxiang; Tang, Jin; Duan, Xuanchu; Zou, Beiji; Jiang, Lingzi

doi:10.1049/iet-ipr.2019.0137

Cited by 9 publications

(1 citation statement)

References 25 publications

(40 reference statements)

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“…The comparison reveals that our proposed system outperforms the existing techniques. In [48], ORIGA dataset has been used and 82.80% accuracy has been attained. In [49], two datasets have been used for training and validation, namely RIM-ONE V3 and DRISHTI-GS, attaining the F1 score 82% and 85%, respectively.…”

Section: Comparative Analysis With State-of-the-artmentioning

confidence: 99%

A Novel Hybrid Approach Based on Deep CNN to Detect Glaucoma Using Fundus Imaging

et al. 2021

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Glaucoma is one of the eye diseases stimulated by the fluid pressure that increases in the eyes, damaging the optic nerves and causing partial or complete vision loss. As Glaucoma appears in later stages and it is a slow disease, detailed screening and detection of the retinal images is required to avoid vision forfeiture. This study aims to detect glaucoma at early stages with the help of deep learning-based feature extraction. Retinal fundus images are utilized for the training and testing of our proposed model. In the first step, images are pre-processed, before the region of interest (ROI) is extracted employing segmentation. Then, features of the optic disc (OD) are extracted from the images containing optic cup (OC) utilizing the hybrid features descriptors, i.e., convolutional neural network (CNN), local binary patterns (LBP), histogram of oriented gradients (HOG), and speeded up robust features (SURF). Moreover, low-level features are extracted using HOG, whereas texture features are extracted using the LBP and SURF descriptors. Furthermore, high-level features are computed using CNN. Additionally, we have employed a feature selection and ranking technique, i.e., the MR-MR method, to select the most representative features. In the end, multi-class classifiers, i.e., support vector machine (SVM), random forest (RF), and K-nearest neighbor (KNN), are employed for the classification of fundus images as healthy or diseased. To assess the performance of the proposed system, various experiments have been performed using combinations of the aforementioned algorithms that show the proposed model based on the RF algorithm with HOG, CNN, LBP, and SURF feature descriptors, providing <=99% accuracy on benchmark datasets and 98.8% on k-fold cross-validation for the early detection of glaucoma.

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Section: Comparative Analysis With State-of-the-artmentioning

confidence: 99%