New computer aided diagnosis system for glaucoma disease based on twin support vector machine

Cheriguene, Soraya; Azizi, Nabiha; Djellali, Hayet; Bunakhla, Oulfa; Aldwairi, Monther; Ziani, Amel

doi:10.1109/edis.2017.8284039

Cited by 9 publications

(3 citation statements)

References 16 publications

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“…For more validation proposed algorithm compared with other glaucoma detection method used the same databases (RIM-ONE, DRISHTI-GS), the results shown in Table.2. [15] achieved 100% accuracy, sensitivity and specificity using small database 13 healthy images 13 glaucoma images, and [13] get accuracy 98.5% using twin support vector machine (TWSVM), where the TWSVM solves a pair of non-parallel hyper-planes whereas and [6] achieved accuracy rate of 97.67% with 98% sensitivity using a multilayer perceptron model classifier, where the SVM solves a single complex one and that prove the accuracy can further improved using different classifier or combined many classifiers together.…”

Section: Resultsmentioning

confidence: 99%

“…Direction degrees we need to calculate the direction of the gradient vector is calculated at each pixel. And the direction of the vector mode are defined as: |∆G| = (|∆H|+|∆v|)/2 θ = tan -1 (∆v/∆H)+0.5π (13) where in ΔH and ΔV are the following two 4×4 operator variation resulting horizontal and vertical directions by the image convolution.…”

Section: Texture Featuresmentioning

confidence: 99%

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Developing a Real Time Algorithm for Diagnosing Glaucoma

Ahmed¹,

Supervisor²,

Co-Supervisor³

2018

IJIVP

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A Glaucoma is a group of eye diseases causing optic nerve damage and if not detected at an early stage it may cause permanent blindness. Glaucoma progression precedes some structural damage to the retina are the symptoms of Glaucoma. Manually, it is diagnosed by examination of size, structure, shape, and color of optic disc and optic cup and retinal nerve fiber layer (RNFL), which suffer from the subjectivity of human due to experience, fatigue factor etc., and with the widespread of higher quality medical imaging techniques, there are increasing demands for computer-aided diagnosis (CAD) systems for glaucoma detection, because the human mistakes, other retinal diseases like Age-related Macular Degeneration (AMD) affecting in early glaucoma detection, and the existing medical devices like Optical Coherence Tomography (OCT) and Heidelberg Retinal Tomography (HRT) are expensive. This paper proposes a novel algorithm by extract 13 shape features from disc and cup, extract 25 texture features from RNFL(retinal nerve fiber layer) using gray level co-occurrence method and Tamara algorithm and 3 color features for each of disc and cup and RNFL. Next, best features selected using two methods, first method is the student t-test and the second method applied was the Sequential Feature Selection (SFS) to introduce the best 6 features. The evaluation of proposed algorithm is performed using a RIM_ONE and DRISHTI-GS databases, the average accuracy 97%, maximize area under curve (AUC) 0.99, specificity 96.6% and sensitivity 98.4% using support vector machine classifier (SVM). Future works suggested to design a complete, automated system not just diagnose glaucoma but calculate the progress of the disease too.

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Section: Resultsmentioning

confidence: 99%

Section: Texture Featuresmentioning

confidence: 99%

Developing a Real Time Algorithm for Diagnosing Glaucoma

Ahmed¹,

Supervisor²,

Co-Supervisor³

2018

IJIVP

View full text Add to dashboard Cite

show abstract

“…Therefore, introducing computer-aided diagnostic (CAD) systems as an important aid to physicians has become particularly urgent. CAD systems play an indispensable role in ensuring an accurate, reliable, and rapid diagnosis of glaucoma [4]. Glaucoma CAD systems can use retinal fundus images as input and classify them as "abnormal" or "normal" by extracting information from multiple feature types.…”

Section: Introductionmentioning

confidence: 99%

An Automatic Glaucoma Grading Method Based on Attention Mechanism and EfficientNet-B3 Network

Zhang,

Lai,

Chen

et al. 2023

Preprint

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Deep learning has received considerable attention in the computer vision field and has been widely studied, especially in recognizing and diagnosing ophthalmic diseases. Currently, glaucoma recognition algorithms are mostly based on unimodal OCT, the visual field for glaucoma auxiliary diagnosis. Such algorithms have poor robustness and limited help for glaucoma auxiliary diagnosis; therefore, this experiment is proposed to use a 2D fundus image and 3D-OCT scanner two modal data as the experimental dataset and use the EfficientNet-B3 network and ResNet34 network models for feature extraction and fusion to improve automatic glaucoma grading accuracy. Since fundus images usually contain a large number of meaningless black background regions, this may lead to feature redundancy. Therefore, this experiment employs an attention mechanism that focuses the attention of the convolutional neural network on eye subject features to improve the performance of the glaucoma autoclassification model.

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