Approaches for Early Detection of Glaucoma Using Retinal Images: A Performance Analysis

Sarhan, Abdullah; Rokne, Jon G.; Alhajj, Reda

doi:10.1007/978-3-030-32587-9_13

Cited by 4 publications

(3 citation statements)

References 40 publications

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“…Approaches related to the first three categories mainly fail in the presence of bright objects similar to the ones shown in Fig. 1 [9]. The red arrows in Fig.…”

Section: Introductionmentioning

confidence: 78%

Utilizing Transfer Learning and a Customized Loss Function for Optic Disc Segmentation from Retinal Images

Sarhan¹,

Ali²,

Gorner³

et al. 2020

Preprint

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Accurate segmentation of the optic disc from a retinal image is vital to extracting retinal features that may be highly correlated with retinal conditions such as glaucoma. In this paper, we propose a deep-learning based approach capable of segmenting the optic disc given a high-precision retinal fundus image. Our approach utilizes a UNETbased model with a VGG16 encoder trained on the ImageNet dataset. This study can be distinguished from other studies in the customization made for the VGG16 model, the diversity of the datasets adopted, the duration of disc segmentation, the loss function utilized, and the number of parameters required to train our model. Our approach was tested on seven publicly available datasets augmented by a dataset from a private clinic that was annotated by two Doctors of Optometry through a web portal built for this purpose. We achieved an accuracy of 99.78% and a Dice coefficient of 94.73% for a disc segmentation from a retinal image in 0.03 seconds. The results obtained from comprehensive experiments demonstrate the robustness of our approach to disc segmentation of retinal images obtained from different sources.

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“…Approaches related to the first three categories mainly fail in the presence of bright objects similar to the ones shown in Fig. 1 [9]. The red arrows in Fig.…”

Section: Introductionmentioning

confidence: 78%

Utilizing Transfer Learning and a Customized Loss Function for Optic Disc Segmentation from Retinal Images

Sarhan¹,

Ali²,

Gorner³

et al. 2020

Preprint

Self Cite

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“…The second direction is binary CAD systems dedicated to identifying age-related macular degeneration (AMD) disease [9]. The third direction is binary retinal CAD systems mainly devoted to segmenting the optic disc (OD) region [3]. The fourth direction includes multi-class CAD systems devoted to grading the progress levels of only DR [14], or only glaucoma [15].…”

Section: Related Workmentioning

confidence: 99%

“…Diabetic retinopathy (DR) and glaucoma, which are the focus of the present work, are regarded as the most important causes of blindness, mainly due to the progress in life prospects and permissive lifestyles [3]. DR is a complication of diabetes that inevitably leads to the terminal state of vision loss.…”

Section: Introductionmentioning

confidence: 99%

Accurate Diagnosis of Diabetic Retinopathy and Glaucoma Using Retinal Fundus Images Based on Hybrid Features and Genetic Algorithm

2021

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Diabetic retinopathy (DR) and glaucoma can both be incurable if they are not detected early enough. Therefore, ophthalmologists worldwide are striving to detect them by personally screening retinal fundus images. However, this procedure is not only tedious, subjective, and labor-intensive, but also error-prone. Worse yet, it may not even be attainable in some countries where ophthalmologists are in short supply. A practical solution to this complicated problem is a computer-aided diagnosis (CAD) system—the objective of this work. We propose an accurate system to detect at once any of the two diseases from retinal fundus images. The accuracy stems from two factors. First, we calculate a large set of hybrid features belonging to three groups: first-order statistics (FOS), higher-order statistics (HOS), and histogram of oriented gradient (HOG). Then, these features are skillfully reduced using a genetic algorithm scheme that selects only the most relevant and significant of them. Finally, the selected features are fed to a classifier to detect one of three classes: DR, glaucoma, or normal. Four classifiers are tested for this job: decision tree (DT), naive Bayes (NB), k-nearest neighbor (kNN), and linear discriminant analysis (LDA). The experimental work, conducted on three publicly available datasets, two of them merged into one, shows impressive performance in terms of four standard classification metrics, each computed using k-fold crossvalidation for added credibility. The highest accuracy has been provided by DT—96.67% for DR, 100% for glaucoma, and 96.67% for normal.

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Utilizing Transfer Learning and a Customized Loss Function for Optic Disc Segmentation from Retinal Images

Sarhan

Ali

Gorner

et al. 2021

Lecture Notes in Computer Science

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Approaches for Early Detection of Glaucoma Using Retinal Images: A Performance Analysis

Cited by 4 publications

References 40 publications

Utilizing Transfer Learning and a Customized Loss Function for Optic Disc Segmentation from Retinal Images

Utilizing Transfer Learning and a Customized Loss Function for Optic Disc Segmentation from Retinal Images

Accurate Diagnosis of Diabetic Retinopathy and Glaucoma Using Retinal Fundus Images Based on Hybrid Features and Genetic Algorithm

Utilizing Transfer Learning and a Customized Loss Function for Optic Disc Segmentation from Retinal Images

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