Abstract
Glaucoma is one of the leading causes of blindness for people over the age of 60. For the automatic glaucoma detection using fundus images, several approaches have been recently developed. However, the extraction of optic cup boundary considered as critical work due to the blood vessels interweavement and also the significant challenges in accurate localization of affected optical disc based on size of the cup which is either too small or large. To overcome this challenges efficient technique needs to be considered. To accurately detect the glaucoma and correspondingly the optic cup OC and optic disk OD boundary the ensemble based CNN classification with spatially based ellipse fitting curve model (SBEFCM) has been proposed. In this study, the glaucoma or diabetes retinopathy DR classification has been performed by the ensemble CNN classification. The optic disc and optic cup boundary detected by new spatially weighted ellipse fitting model SBEFCM. For the proposed SBEFCM, which enhances and prolongs the multi-ellipse fitting technique. The ensemble based CNN classification is performed for various datasets such as LAG, RIM-ONE and local retinal glaucoma dataset. Previously the input fundus image is pre-processed and further blood vessel segmentation performed to avoid the interweavement with surrounding tissues and blood vessels. The new SBEFCM accurately detected the ratio of OC and OD boundary prediction which are depicted and by using the ensemble CNN classification, various performance metrics such as sensitivity, specificity, accuracy and AUC values has been identified for glaucoma detection and by comparison the proposed ensemble CNN outperforms the existing methods.
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References
Aamir M, Irfan M, Ali T, Ali G, Shaf A, Al-Beshri A, Alasbali T, Mahnashi MH (2020) An adoptive threshold-based multi-level deep convolutional neural network for glaucoma eye disease detection and classification. Diagnostics 10:602
Al-Bander B, Williams BM, Al-Nuaimy W, Al-Taee MA, Pratt H, Zheng Y (2018) Dense fully convolutional segmentation of the optic disc and cup in colour fundus for glaucoma diagnosis. Symmetry 10:87
Almubarak H, Bazi Y, Alajlan N (2020) Two-stage mask-RCNN approach for detecting and segmenting the optic nerve head, optic disc, and optic cup in fundus images. Appl Sci 10:3833
Bhatkalkar BJ, Reddy DR, Prabhu S, Bhandary SV (2020) Improving the performance of convolutional neural network for the segmentation of optic disc in fundus images using attention gates and conditional random fields. IEEE Access 8:29299–29310
David DS (2019) Parasagittal meningioma brain tumor classification system based on MRI images and multi phase level set formulation. Biomed Pharmacol J 12:939–946
David MDS, Jayachandran A (2018) Robust classification of brain tumor in MRI images using salient structure descriptor and RBF kernel-SVM. J Graphic Technol 14:718–737
David DS, Jayachandran A (2020) A new expert system based on hybrid colour and structure descriptor and machine learning algorithms for early glaucoma diagnosis. Multim Tools Appl 79:5213–5224
David DS, Jeyachandran AA (2016) Comprehensive survey of security mechanisms in healthcare applications. In: 2016 international conference on communication and electronics systems (ICCES), 2016. IEEE, pp 1–6
Fu H, Cheng J, Xu Y, Wong DWK, Liu J, Cao X (2018) Joint optic disc and cup segmentation based on multi-label deep network and polar transformation. IEEE Trans Med Imaging 37:1597–1605
Fu H, Cheng J, Xu Y, Liu J (2019) Glaucoma detection based on deep learning network in fundus image. In: Deep learning and convolutional neural networks for medical imaging and clinical informatics. Springer
Gao Y, Yu X, Wu C, Zhou W, Wang X, Chu H (2019) Accurate and efficient segmentation of optic disc and optic cup in retinal images integrating multi-view information. IEEE Access 7:148183–148197
García G, Del Amor R, Colomer A, Naranjo V (2020) Glaucoma detection from raw circumpapillary OCT images using fully convolutional neural networks. In: 2020 IEEE international conference on image processing (ICIP), 2020. IEEE, pp 2526–2530
Guo F, Mai Y, Zhao X, Duan X, Fan Z, Zou B, Xie B (2018) Yanbao: a mobile app using the measurement of clinical parameters for glaucoma screening. IEEE Access 6:77414–77428
Jayachandran A, David DS (2018) Textures and intensity histogram based retinal image classification system using hybrid colour structure descriptor. Biomed Pharmacol J 11:577–582
Jiang Y, Duan L, Cheng J, Gu Z, Xia H, Fu H, Li C, Liu J (2019) Jointrcnn: a region-based convolutional neural network for optic disc and cup segmentation. IEEE Trans Biomed Eng 67:335–343
Kumar S, Pathak S, Kumar B (2019) Automated detection of eye related diseases using digital image processing. In: Handbook of multimedia information security: techniques and applications. Springer
Li L, Xu M, Liu H, Li Y, Wang X, Jiang L, Wang Z, Fan X, Wang N (2019) A large-scale database and a CNN model for attention-based glaucoma detection. IEEE Trans Med Imaging 39:413–424
Liu Q, Hong X, Li S, Chen Z, Zhao G, Zou B (2019) A spatial-aware joint optic disc and cup segmentation method. Neurocomputing 359:285–297
Orlando JI, Fu H, Breda JB, Van Keer K, Bathula DR, Diaz-Pinto A, Fang R, Heng P-A, Kim J, Lee J (2020) Refuge challenge: a unified framework for evaluating automated methods for glaucoma assessment from fundus photographs. Med Image Anal 59:101570
Qureshi I (2015) Glaucoma detection in retinal images using image processing techniques: a survey. Int J Adv Netw Appl 7:2705
Qureshi I, Ma J, Abbas Q (2019) Recent development on detection methods for the diagnosis of diabetic retinopathy. Symmetry 11:749
Raja H, Akram MU, Shaukat A, Khan SA, Alghamdi N, Khawaja SG, Nazir N (2020) Extraction of retinal layers through convolution neural network (CNN) in an OCT image for glaucoma diagnosis. J Dig Imaging 33:1428–1442
Rehman ZU, Naqvi SS, Khan TM, Arsalan M, Khan MA, Khalil M (2019) Multi-parametric optic disc segmentation using superpixel based feature classification. Expert Syst Appl 120:461–473
Salamat N, Missen MMS, Rashid A (2019) Diabetic retinopathy techniques in retinal images: a review. Artif Intell Med 97:168–188
Thakur N, Juneja M (2018) Survey on segmentation and classification approaches of optic cup and optic disc for diagnosis of glaucoma. Biomed Signal Process Control 42:162–189
Vaghjiani D, Saha S, Connan Y, Frost S, Kanagasingam Y (2020) Visualizing and understanding inherent image features in CNN-based glaucoma detection. In: 2020 digital image computing: techniques and applications (DICTA), 2020. IEEE, pp 1–3
Veena H, Muruganandham A, Kumaran TS (2021) A novel optic disc and optic cup segmentation technique to diagnose glaucoma using deep learning convolutional neural network over retinal fundus images. J King Saud Univ Comput Inf Sci. https://doi.org/10.1016/j.jksuci.2021.02.003
Zhang L, Lim CP (2020) Intelligent optic disc segmentation using improved particle swarm optimization and evolving ensemble models. Appl Soft Comput 92:106328
Zhao R, Liao W, Zou B, Chen Z, Li S (2019) Weakly-supervised simultaneous evidence identification and segmentation for automated glaucoma diagnosis. In: Proceedings of the AAAI conference on artificial intelligence, 2019, pp 809–816
Zhao R, Li S (2020) Multi-indices quantification of optic nerve head in fundus image via multitask collaborative learning. Med Image Anal 60:101593
Zilly J, Buhmann JM, Mahapatra D (2017) Glaucoma detection using entropy sampling and ensemble learning for automatic optic cup and disc segmentation. Comput Med Imaging Graph 55:28–41
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David, D.S. Enhanced glaucoma detection using ensemble based CNN and spatially based ellipse fitting curve model. J Ambient Intell Human Comput 14, 3303–3314 (2023). https://doi.org/10.1007/s12652-021-03467-4
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DOI: https://doi.org/10.1007/s12652-021-03467-4