Glaucoma classification with a fusion of segmentation and image-based features

Chakravarty, Arunava; Sivaswamy, Jayanthi

doi:10.1109/isbi.2016.7493360

Cited by 45 publications

(34 citation statements)

References 12 publications

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“…Table III and Table IV discussed performance of the proposed GlaucoNet+ model with different classification setups (Table III) and configuration (Table IV); however to further examine efficacy of our proposed model in comparison to the other existing glaucoma detection and classification methods, we have performed analysis based on secondary resources (reviewing existing methods or allied papers). [49] 84.38 --- [54] 95.50 --- [60] --- [64] 88.00 --- [35] --98.60 - [36] 99.20 -86.00 - [65] ---- [47] 80.00 -95.00 - [48] 97.00 --- [57] 98 --- [39] 94.10 -91.80 - [75] 90.00 --- [40] --92.00 - [33] 100.00 -94.00 - [59] ---- [66] 89.6 (NB) 97.6(AN N) --- [67] 92.00 --- [68] ---- [69] 72.38 --- [70] 79.00 -87.00 - [71] 83.10 --- [62] 93.00 --- [72] 96.67 -100.00 - [73] 91.00 --- [74] 92.00 --- [4] 0.8478 - [55] 88. Observing the results, it can be found that the proposed GlaucoNet+ model with Hybrid feature extraction and SVM (polynomial) with 10-fold cross validation outperforms major existing approaches.…”

Section: Resultsmentioning

confidence: 99%

Hybrid CNN assisted Computer Aided Diagnosis System for Glaucoma Detection and Classification: GlaucoNet+

2019

IJITEE

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The exponential rise in technologies has revitalized academia-industries to achieve more efficient computer aided diagnosis systems. It becomes inevitable especially for Glaucoma detection which has been increasing with vast pace globally. Most of the existing approaches employs morphological features like optical disk and optical cup information, optical cup to disk ratio etc; however enabling optimal detection of such traits has always been challenge for researchers. On the other hand, in the last few years deep learning methods have gained widespread attention due to its ability to exploit fine grained features of images to make optimal classification decision. However, reliance of such methods predominantly depends on the presence of deep features demanding suitable feature extraction method. To achieve it major existing approaches extracts full-image features that with high dimensional kernel generates gigantically huge features, making classification computationally overburdened. Therefore, retaining optimal balance between deep features and computational overhead is of utmost significance for glaucoma detection and classification. With this motive, in this paper a novel hybrid deep learning model has been developed for Glaucoma detection and classification. The proposed Hybrid CNN model embodies Stacked Auto-Encoder (SAE) with transferable learning model AlexNet that extracts high dimensional features to make further two-class classification. To achieve computational efficiency, In addition to the classical ReLu and dropout (50%), we used Principle Component Analysis (PCA) and Linear Discriminant Analysis (LDA) algorithms. We applied 10-fold cross validation assisted Support Vector Machine classifier to perform two-class classification; Glaucomatous and Normal fundus images. Simulation results affirmed that the proposed Hybrid deep learning model with LDA feature selection and SVM-Poly classification achieves the maximum accuracy of 98.8%, precision 97.5%, recall 97.5% and F-Measure of 97.8%.

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Section: Resultsmentioning

confidence: 99%

Hybrid CNN assisted Computer Aided Diagnosis System for Glaucoma Detection and Classification: GlaucoNet+

2019

IJITEE

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“…We can increase the number of patients and analyze the performance. [2] In this study document, the various image processing techniques used for the detection and diagnosis of glaucoma were compiled. The main objective here is to highlight the extensive research conducted in this area and to highlight the seriousness of this disease.…”

Section: Literature Reviewmentioning

confidence: 99%

Hybrid Algorithm of Retinal Blood Vessel Detection, Health Estimation, Optic Disc Segmentation and Glaucoma Detection

Rathore¹,

Sharma²,

Balai³

2017

IJERT

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Human Eye is one of the most important sensor organs of human being, thus diseases and aliments of human Eye have a most important on a patient's life and thus advanced detection and prevention techniques are required, Diabetes is a contributing factor in causing eye aliments and catalyzing them. Two of the major eye diseases that possess saver threat to vision are Glaucoma and Micro-aneurysm. Glaucoma causes damage to the eyes optic nerve and worsens with time. Glaucoma has no early symptoms as pain, if continues untreated can cause per moment loss of vision micro-aneurysm is a swelling that occurs in the wall of capillary blood vessels. These small swelling may reports and allow blood leak in near by tissue. People suffering from diabetes may get micro aneurysm in the retina and can progress into vision loss. As Glaucoma and Micro aneurysm are not can able, but preventable diseases there early detection is the key for good prognosis on this work. We have propose on advanced algorithm using combination of image processing and artificial intelligence for early detection of Glaucoma and Micro Aneurysm. The steps accomplished are retinal blood vessel detection, Optic Disc segmentation optic disc and Optic cup detection CPR (cup disc ratio) computation and use of artificial neural network for training prediction.

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“…Several authors prefer the use of more than two colour planes usually from RGB colour space. The processing is performed separately, as if different grey level images were available [ 8 , 10 , 43 , 44 ].…”

Section: Introductionmentioning

confidence: 99%

“…In these cases, the cup edge is dictated by vessel bends. For this reason, the majority of approaches presented in the past may not give accurate results when dealing with complex image databases [ 5 , 8 , 10 , 26 – 28 , 30 – 34 , 40 , 41 , 44 , 49 , 51 – 54 ].…”

Section: Introductionmentioning

confidence: 99%

Automatic CDR Estimation for Early Glaucoma Diagnosis

Fernández-Granero

Sarmiento

Morillo

et al. 2017

Journal of Healthcare Engineering

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Glaucoma is a degenerative disease that constitutes the second cause of blindness in developed countries. Although it cannot be cured, its progression can be prevented through early diagnosis. In this paper, we propose a new algorithm for automatic glaucoma diagnosis based on retinal colour images. We focus on capturing the inherent colour changes of optic disc (OD) and cup borders by computing several colour derivatives in CIE L∗a∗b∗ colour space with CIE94 colour distance. In addition, we consider spatial information retaining these colour derivatives and the original CIE L∗a∗b∗ values of the pixel and adding other characteristics such as its distance to the OD centre. The proposed strategy is robust due to a simple structure that does not need neither initial segmentation nor removal of the vascular tree or detection of vessel bends. The method has been extensively validated with two datasets (one public and one private), each one comprising 60 images of high variability of appearances. Achieved class-wise-averaged accuracy of 95.02% and 81.19% demonstrates that this automated approach could support physicians in the diagnosis of glaucoma in its early stage, and therefore, it could be seen as an opportunity for developing low-cost solutions for mass screening programs.

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Glaucoma classification with a fusion of segmentation and image-based features

Cited by 45 publications

References 12 publications

Hybrid CNN assisted Computer Aided Diagnosis System for Glaucoma Detection and Classification: GlaucoNet+

Hybrid CNN assisted Computer Aided Diagnosis System for Glaucoma Detection and Classification: GlaucoNet+

Hybrid Algorithm of Retinal Blood Vessel Detection, Health Estimation, Optic Disc Segmentation and Glaucoma Detection

Automatic CDR Estimation for Early Glaucoma Diagnosis

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