Glaucoma Detection from Retinal Images using Generic Features

Talaat, Mennato-Allah; Raed, Nataly; Medhat, Aya; Ashraf, Romisaa; Essam, Mohammad; ElKashlan, Rana; Abdel-Hamid, Lamiaa

doi:10.1145/3369973.3369976

Cited by 5 publications

(8 citation statements)

References 17 publications

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“…[18][19][20] Generic methods overcome the limitations of structural methods by relying on a combination of statistical and textural features in order to capture the overall characteristics of the ONH region. In literature, generic features were computed either directly from the spatial channels of the retinal images [21][22][23][24] or from their wavelet subbands. [25][26][27] Generic features were sometimes also combined with structural measures for glaucoma detection.…”

Section: Literature Reviewmentioning

confidence: 99%

TWEEC: Computer‐aided glaucoma diagnosis from retinal images using deep learning techniques

Abdel-Hamid

2021

Int J Imaging Syst Tech

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A novel two-branched deep convolutional (TWEEC) network is developed for computer-aided glaucoma diagnosis. The TWEEC network is designed to simultaneously extract anatomical information related to the optic disc and surrounding blood vessels which are the retinal structures most affected by glaucoma progression. The spatial retinal images and wavelet approximation subbands are compared as inputs to the proposed network. TWEEC's performance is compared to three implemented convolutional networks, one of which employs transfer learning. Experiments showed that the introduced TWEEC network achieved accuracies of 98.78% and 96.34% for the spatial and wavelet inputs, respectively, by that outperforming the other three deep networks by 8-15%. This work paves the way for the development of efficient deep learning based computer-aided glaucoma diagnosis tools. Moreover, the present study illustrates that considering specific wavelet subbands for the training of convolutional networks can result in reliable performance with the advantage of reduced overall network training time.

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Section: Literature Reviewmentioning

confidence: 99%

TWEEC: Computer‐aided glaucoma diagnosis from retinal images using deep learning techniques

Abdel-Hamid

2021

Int J Imaging Syst Tech

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“…We chose these classifiers for presenting satisfactory results in the studies from [5], [8] and [19]. Also, in comparison with other classifiers by grid search techniques and exhaustive comparison tests, the chosen classifiers got the best results.…”

Section: Methodsmentioning

confidence: 99%

“…In the related works, authors that chose to extract features non-structural to describe images used statistical information, texture, and entropy, among others. For example, in work developed by Talaat et al [5], the authors applied features extraction using statistical and texture information. After that, they reduced the amount of data to balance the classes, and performed the classification using SVM.…”

Section: Related Workmentioning

confidence: 99%

“…To our best knowledge, the most used method to validate the results is k-fold cross-validation, as [5] used 10-fold for the training and validation step. Other works that used 10-fold to validate are [6], [9], [10], [14] and [18].…”

Section: Related Workmentioning

confidence: 99%

“…Most public datasets with glaucoma image labels are imbalanced, compromising model training. In our review, only [5] applied undersampling. They used 40 images for each class and 10-fold cross-validation to obtain results, that is, fewer test images.…”

Section: Related Workmentioning

confidence: 99%

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A Comparative Analysis of Glaucoma Feature Extraction and Classification Techniques in Fundus Images

Assis

Cortez

2023

JCIS

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Glaucoma is an asymptomatic chronic eye disease that, if not treated in the early stages, can lead to blindness. Therefore, detection in the early stages is essential to preserve the patient’s quality of life. Thus, it is crucial to have a noninvasive method capable of detecting this disease through images in the fundus examination. In the literature, datasets are available with fundus images; however, only a few have glaucoma images and labels. Learning from an imbalanced dataset challenges machine learning, which limits supervised learning algorithms. We compared approaches to extract and classify three public datasets with 2390 images: ACRIMA, REFUGE, and RIM-ONE DL. First, we evaluated extracted features non-structural from HOG, LBP, Zernike, and Gabor filters and features obtained from transfer learning. Then, we classified them with Multilayer Perceptron (MLP), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGB). Finally, each classifier was evaluated individually and in a voting classifier (VOT). We extracted and classified features from transfer learning models in the same process. Also, they were classified using traditional machine learning. Due to class imbalance, we undersampled the majority class normal by applying the following methods: random choice, near miss, and cluster centroid. We also evaluated our model using a cross-dataset approach. Therefore, we efficiently identified glaucoma in different fundus images using network VGG19 and a voting classifier. In addition, balancing classes reduced false negatives and improved model quality. Our approach achieved an average F1-score equals to 94.69%, accuracy rate of 94.77%, precision of 96.10%, recall of 93.45%, and specificity of 96.08%.

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Evaluating machine learning techniques for enhanced glaucoma screening through Pupillary Light Reflex analysis

Pinheiro,

Camilo,

Paranhos

et al. 2024

Array

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Glaucoma Detection from Retinal Images using Generic Features

Cited by 5 publications

References 17 publications

TWEEC: Computer‐aided glaucoma diagnosis from retinal images using deep learning techniques

TWEEC: Computer‐aided glaucoma diagnosis from retinal images using deep learning techniques

A Comparative Analysis of Glaucoma Feature Extraction and Classification Techniques in Fundus Images

Evaluating machine learning techniques for enhanced glaucoma screening through Pupillary Light Reflex analysis

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