Automatic Glaucoma Diagnosis in Digital Fundus Images Using Deep CNNs

An automatic Optic disc and Optic cup detection technique which is an important step in developing systems for computer-aided eye disease diagnosis is presented in this paper. This paper presents an algorithm for localization and segmentation of optic disc from digital retinal images. OD localization is achieved by circular Hough transform using morphological preprocessing and segmentation is achieved by watershed transformation. Optic cup segmentation is achieved by marker controlled watershed transformation. The optic disc to cup ratio (CDR) is calculated which is an important parameter for glaucoma diagnosis. The presented algorithm is evaluated against publically available DRIVE dataset. The presented methodology achieved 88% average sensitivity and 80% average overlap. The average CDR detected is 0.1983.

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Performance Analysis of Optimizers for Glaucoma Diagnosis from Fundus Images Using Transfer Learning

Lecture Notes in Electrical Engineering - Machine Learning, Deep Learning and Computational Intelligence for Wireless Communication ◽

10.1007/978-981-16-0289-4_37 ◽

2021 ◽

pp. 507-518

Author(s):

Poonguzhali Elangovan ◽

Malaya Kumar Nath

Keyword(s):

Performance Analysis ◽

Transfer Learning ◽

Fundus Images ◽

Glaucoma Diagnosis

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An Enhanced Approach for Automated Glaucoma Diagnosis in Retinal Fundus Images

10.1007/978-3-030-89701-7_17 ◽

2021 ◽

pp. 185-197

Author(s):

Osama M. Kamara ◽

Ahmed H. Asad ◽

Hesham A. Hefny

Keyword(s):

Fundus Images ◽

Glaucoma Diagnosis ◽

Retinal Fundus Images ◽

Retinal Fundus

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Automatic Diagnosis for Glaucoma Fusing Multi-Feature and Multi-Classifier

Journal of Medical Imaging and Health Informatics ◽

10.1166/jmihi.2020.3047 ◽

2020 ◽

Vol 10 (7) ◽

pp. 1540-1546

Author(s):

Xiaomei Xu ◽

Xiaobo Lai ◽

Yanli Liu

Keyword(s):

Cross Validation ◽

Maximum Intensity ◽

Eye Disease ◽

Retinal Diseases ◽

Fundus Images ◽

Time Cost ◽

Fusion Model ◽

Glaucoma Diagnosis ◽

Retinal Fundus ◽

Fold Cross Validation

Glaucoma is a chronic and irreversible eye disease leading to blindness, and early detection is particularly important for its diagnosis and treatment. To improve the performance of automatic glaucoma diagnosis, a method based on multi-feature and multi-classifier is proposed. Firstly, an average histogram is obtained for each channel and ophthalmic condition, and 6 features are extracted from the average histogram with the average count of pixels and their maximum intensity value. Secondly, the optimal features combination is screened for each classifier with 10-fold cross-validation. Finally, the three optimal classifiers and their optimal features combination are fused with the principle of democratic voting. With the 10-fold cross-validation algorithm, the fusion model was evaluated on Local and HRF dataset, that achieved accuracy of 91.8% and 93.3%, sensitivity of 86.9% and 86.7%, specificity of 96.7% and 100%, AUC of 0.953 and 0.978, time cost of 1.0 s and 3.9 s per image, respectively. Simulation results show that the proposed method is of high accuracy and generality. It can effectively classify the retinal fundus images and provide technical support for the clinical diagnosis of retinal diseases.

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Few-shot Learning Using a Small-Sized Dataset of High-Resolution FUNDUS Images for Glaucoma Diagnosis

Proceedings of the 2nd International Workshop on Multimedia for Personal Health and Health Care - MMHealth '17 ◽

10.1145/3132635.3132650 ◽

2017 ◽

Cited By ~ 5

Author(s):

Mijung Kim ◽

Jasper Zuallaert ◽

Wesley De Neve

Keyword(s):

High Resolution ◽

Fundus Images ◽

Glaucoma Diagnosis

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