A Two Layer Sparse Autoencoder for Glaucoma Identification with Fundus Images

Glaucoma is a type of eye condition which may result in partial or consummate vision loss. Higher intraocular pressure is the leading cause for this condition. Screening for glaucoma and early detection can avert vision loss. Computer aided diagnosis (CAD) is an automated process with the potential...

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Vydáno v:	Journal of medical systems Ročník 43; číslo 9; s. 299 - 9
Hlavní autoři:	Raghavendra, U., Gudigar, Anjan, Bhandary, Sulatha V., Rao, Tejaswi N., Ciaccio, Edward J., Acharya, U. Rajendra
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York Springer US 01.09.2019 Springer Nature B.V
Témata:	Adult Algorithms Case-Control Studies Diagnostic Techniques, Ophthalmological Digital imaging Female Fundus Oculi Glaucoma Health Informatics Health Informatics and Computer Vision Health Sciences Humans Image & Signal Processing Image Interpretation, Computer-Assisted - methods Intraocular pressure Learning algorithms Machine learning Male Medicine Medicine & Public Health Middle Aged Pattern Recognition, Automated - methods Quantitative analysis Recent Advances in Deep Learning for Biomedical Signal Processing Statistics for Life Sciences Test procedures Tomography, Optical Coherence - methods Vision Glaucoma Sparse autoencoder Cascade CAD
ISSN:	0148-5598, 1573-689X, 1573-689X
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Abstract	Glaucoma is a type of eye condition which may result in partial or consummate vision loss. Higher intraocular pressure is the leading cause for this condition. Screening for glaucoma and early detection can avert vision loss. Computer aided diagnosis (CAD) is an automated process with the potential to identify glaucoma early through quantitative analysis of digital fundus images. Preparing an effective model for CAD requires a large database. This study presents a CAD tool for the precise detection of glaucoma using a machine learning approach. An autoencoder is trained to determine effective and important features from fundus images. These features are used to develop classes of glaucoma for testing. The method achieved an F − measure value of 0.95 utilizing 1426 digital fundus images (589 control and 837 glaucoma). The efficacy of the system is evident, and is suggestive of its possible utility as an additional tool for verification of clinical decisions.
AbstractList	Glaucoma is a type of eye condition which may result in partial or consummate vision loss. Higher intraocular pressure is the leading cause for this condition. Screening for glaucoma and early detection can avert vision loss. Computer aided diagnosis (CAD) is an automated process with the potential to identify glaucoma early through quantitative analysis of digital fundus images. Preparing an effective model for CAD requires a large database. This study presents a CAD tool for the precise detection of glaucoma using a machine learning approach. An autoencoder is trained to determine effective and important features from fundus images. These features are used to develop classes of glaucoma for testing. The method achieved an F − measure value of 0.95 utilizing 1426 digital fundus images (589 control and 837 glaucoma). The efficacy of the system is evident, and is suggestive of its possible utility as an additional tool for verification of clinical decisions. Glaucoma is a type of eye condition which may result in partial or consummate vision loss. Higher intraocular pressure is the leading cause for this condition. Screening for glaucoma and early detection can avert vision loss. Computer aided diagnosis (CAD) is an automated process with the potential to identify glaucoma early through quantitative analysis of digital fundus images. Preparing an effective model for CAD requires a large database. This study presents a CAD tool for the precise detection of glaucoma using a machine learning approach. An autoencoder is trained to determine effective and important features from fundus images. These features are used to develop classes of glaucoma for testing. The method achieved an F − measure value of 0.95 utilizing 1426 digital fundus images (589 control and 837 glaucoma). The efficacy of the system is evident, and is suggestive of its possible utility as an additional tool for verification of clinical decisions. Glaucoma is a type of eye condition which may result in partial or consummate vision loss. Higher intraocular pressure is the leading cause for this condition. Screening for glaucoma and early detection can avert vision loss. Computer aided diagnosis (CAD) is an automated process with the potential to identify glaucoma early through quantitative analysis of digital fundus images. Preparing an effective model for CAD requires a large database. This study presents a CAD tool for the precise detection of glaucoma using a machine learning approach. An autoencoder is trained to determine effective and important features from fundus images. These features are used to develop classes of glaucoma for testing. The method achieved an F - measure value of 0.95 utilizing 1426 digital fundus images (589 control and 837 glaucoma). The efficacy of the system is evident, and is suggestive of its possible utility as an additional tool for verification of clinical decisions. Glaucoma is a type of eye condition which may result in partial or consummate vision loss. Higher intraocular pressure is the leading cause for this condition. Screening for glaucoma and early detection can avert vision loss. Computer aided diagnosis (CAD) is an automated process with the potential to identify glaucoma early through quantitative analysis of digital fundus images. Preparing an effective model for CAD requires a large database. This study presents a CAD tool for the precise detection of glaucoma using a machine learning approach. An autoencoder is trained to determine effective and important features from fundus images. These features are used to develop classes of glaucoma for testing. The method achieved an F - measure value of 0.95 utilizing 1426 digital fundus images (589 control and 837 glaucoma). The efficacy of the system is evident, and is suggestive of its possible utility as an additional tool for verification of clinical decisions.Glaucoma is a type of eye condition which may result in partial or consummate vision loss. Higher intraocular pressure is the leading cause for this condition. Screening for glaucoma and early detection can avert vision loss. Computer aided diagnosis (CAD) is an automated process with the potential to identify glaucoma early through quantitative analysis of digital fundus images. Preparing an effective model for CAD requires a large database. This study presents a CAD tool for the precise detection of glaucoma using a machine learning approach. An autoencoder is trained to determine effective and important features from fundus images. These features are used to develop classes of glaucoma for testing. The method achieved an F - measure value of 0.95 utilizing 1426 digital fundus images (589 control and 837 glaucoma). The efficacy of the system is evident, and is suggestive of its possible utility as an additional tool for verification of clinical decisions.
ArticleNumber	299
Author	Bhandary, Sulatha V. Raghavendra, U. Acharya, U. Rajendra Ciaccio, Edward J. Rao, Tejaswi N. Gudigar, Anjan
Author_xml	– sequence: 1 givenname: U. surname: Raghavendra fullname: Raghavendra, U. organization: Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education – sequence: 2 givenname: Anjan surname: Gudigar fullname: Gudigar, Anjan email: anjan.gudigar@manipal.edu organization: Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education – sequence: 3 givenname: Sulatha V. surname: Bhandary fullname: Bhandary, Sulatha V. organization: Department of Ophthalmology, Kasturba Medical College, Manipal Academy of Higher Education – sequence: 4 givenname: Tejaswi N. surname: Rao fullname: Rao, Tejaswi N. organization: Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education – sequence: 5 givenname: Edward J. surname: Ciaccio fullname: Ciaccio, Edward J. organization: Department of Medicine, Columbia University – sequence: 6 givenname: U. Rajendra surname: Acharya fullname: Acharya, U. Rajendra organization: Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Department of Biomedical Engineering, School of Science and Technology, SUSS University, School of Medicine, Faculty of Health and Medical Sciences, Taylor’s University
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Cites_doi	10.1016/j.bspc.2013.11.006 10.1109/JBHI.2016.2544961 10.1109/INDICON.2015.7443150 10.1016/j.cmpb.2018.07.012 10.1109/ICICES.2014.7033743 10.1159/000303213 10.1001/archopht.1991.01080080050026 10.1016/j.bbe.2017.11.002 10.1109/CBMS.2012.6266344 10.1016/j.ins.2017.10.044 10.1109/EMBC.2015.7318462 10.1016/j.compbiomed.2017.06.017 10.1016/j.media.2004.12.004 10.1001/archopht.124.11.1579 10.1016/j.compmedimag.2007.11.003 10.1016/S0042-6989(97)00169-7 10.1142/S0219519413500115 10.1109/TITB.2011.2176540 10.1016/j.compbiomed.2018.11.028 10.1016/j.compbiomed.2017.06.022 10.1007/s10916-008-9195-z 10.1136/bjo.2005.081224 10.1109/TPAMI.2012.277 10.1016/j.knosys.2012.02.010 10.1016/j.ins.2018.01.051 10.1001/archopht.122.4.532 10.1155/2018/8676387 10.1016/j.bspc.2014.09.004 10.1109/TITB.2011.2119322
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References	Gajbhiye, Gaurav O., and Kamthane, Ashok N., Automatic classification of glaucomatous images using wavelet and moment feature. In India Conference (INDICON), 2015 Annual IEEE, pp. 1–5. IEEE, 2015. AcharyaURDuaSDuXSreeSVChuaCKAutomated diagnosis of glaucoma using texture and higher order spectra featuresIEEE Trans Inf Technol Biomed201115344945510.1109/TITB.2011.211932221349793 Yin, Fengshou, Liu, Jiang, Wong, Damon Wing Kee, Tan, Ngan Meng, Cheung, Carol, Baskaran, Mani, Aung, Tin, and Wong, Tien Yin., Automated segmentation of optic disc and optic cup in fundus images for glaucoma diagnosis. In Computer-based medical systems (CBMS), 2012 25th international symposium on, pp. 1–6. IEEE, 2012. AcharyaURNgEYKEugeneLWJNoronhaKPMinLCNayakKPBhandarySVDecision support system for the glaucoma using Gabor transformationBiomedical Signal Processing and Control201515182610.1016/j.bspc.2014.09.004 AnnuNJustinJAutomated classification of glaucoma images by wavelet energy featuresInternational Journal of Engineering and Technology20135217161721 BurgoyneCFPearls of glaucoma management2010Berlin, HeidelbergSpringer(Chapter 1. p. 1) NayakJAcharyaRSubbanna BhatPShettyNLimT-CAutomated diagnosis of glaucoma using digital fundus imagesJ. Med. Syst.200933533710.1007/s10916-008-9195-z19827259 MaheshwariSPachoriRBAcharyaURAutomated diagnosis of glaucoma using empirical wavelet transform and correntropy features extracted from fundus imagesIEEE journal of biomedical and health informatics201621380381310.1109/JBHI.2016.254496128113877 Maheshwari, S., Kanhangad, V., Pachori, R. B., Bhandary, S. V., and Acharya, U. R., Automated glaucoma diagnosis using bit-plane slicing and local binary pattern techniques. Comput. Biol. Med., 2018 (In Press). QuigleyHABromanATThe number of people with glaucoma worldwide in 2010 and 2020Br. J. Ophthalmol.20069032622671:STN:280:DC%2BD28%2FpvFOjsA%3D%3D10.1136/bjo.2005.081224164889401856963 Gayathri, R., Rao, P. V., and Aruna, S., Automated glaucoma detection system based on wavelet energy features and ANN. In Advances in Computing, Communications and Informatics (ICACCI, 2014 International Conference on, pp. 2808–2812. IEEE, 2014. Fink, F., Worle, K., Gruber, P., Tome, A. M., Gorriz-Saez, J. M., Puntonet, C. G., and Lang, E. W., ICA analysis of retina images for glaucoma classification. In Engineering in Medicine and Biology Society, 2008. EMBS 2008. 30th Annual International Conference of the IEEE, pp. 4664–4667. IEEE, 2008. DuaSAcharyaURChowriappaPVinitha SreeSWavelet-based energy features for glaucomatous image classificationIEEE Trans. Inf. Technol. Biomed.2012161808710.1109/TITB.2011.217654022113813 MookiahMRKAcharyaURLimCMPetznickASuriJSData mining technique for automated diagnosis of glaucoma using higher order spectra and wavelet energy featuresKnowl.-Based Syst.201233738210.1016/j.knosys.2012.02.010 GayathriRRaoPVGlaucoma detection using cup to disc ratio and artificial neural networksInternational Journal of Engineering & Technology20187135140 MatsopoulosGKAsvestasPADelibasisKKMouravlianskyNAZeyenTGDetection of glaucomatous change based on vessel shape analysisComput. Med. Imaging Graph.200832318319210.1016/j.compmedimag.2007.11.00318187308 ShinH-COrtonMRCollinsDJDoranSJLeachMOStacked autoencoders for unsupervised feature learning and multiple organ detection in a pilot study using 4D patient dataIEEE Trans. Pattern Anal. Mach. Intell.20133581930194310.1109/TPAMI.2012.27723787345 YangJBaiYLiGLiuMLiuXA novel method of diagnosing premature ventricular contraction based on sparse autoencoder and softmax regressionBiomed. Mater. Eng.201526S1549S155826405919 ChenLZhouMSuWWuMSheJHirotaKSoftmax regression based deep sparse autoencoder network for facial emotion recognition in human-robot interactionInf. Sci.2018428496110.1016/j.ins.2017.10.044 RaghavendraUFujitaHBhandarySVGudigarATanJHAcharyaURDeep convolution neural network for accurate diagnosis of glaucoma using digital fundus imagesInf. Sci.2018441414910.1016/j.ins.2018.01.051 NikiasCLPetropuluAPHigher-order spectra analysis: A nonlinear signal processing framework1993Englewood Cliffs, NJPTR Prentice Hall KrishnanMMRFaustOAutomated glaucoma detection using hybrid feature extraction in retinal fundus imagesJournal of Mechanics in Medicine and Biology20131301135001110.1142/S0219519413500115 HagiwaraYKohJEWTanJHBhandarySVLaudeACiaccioEJTongLAcharyaURComputer-aided diagnosis of glaucoma using fundus images: A reviewComput. Methods Prog. Biomed.201816511210.1016/j.cmpb.2018.07.012 GoldmannHSchmidtTApplanation tonometryOphthalmologica19571342212421:STN:280:DyaG1c%2Fis12isw%3D%3D10.1159/00030321313484216 Simonthomas, S., Thulasi, N., and Asharaf, P., Automated diagnosis of glaucoma using Haralick texture features. In Information Communication and Embedded Systems (ICICES), 2014 International Conference on, pp. 1–6. IEEE, 2014. RaghavendraUBhandarySVGudigarAAcharyaURNovel expert system for glaucoma identification using non-parametric spatial envelope energy spectrum with fundus imagesBiocybernetics and Biomedical Engineering201738117018010.1016/j.bbe.2017.11.002 NoronhaKPAcharyaURPrabhakar NayakKMartisRJBhandarySVAutomated classification of glaucoma stages using higher order cumulant featuresBiomedical Signal Processing and Control20141017418310.1016/j.bspc.2013.11.006 SommerATielschJMKatzJQuigleyHAGottschJDJavittJSinghKRelationship between intraocular pressure and primary open angle glaucoma among white and black Americans. The Baltimore eye surveyArch. Ophthalmol.19911098109010951:STN:280:DyaK3MzivVKnsw%3D%3D10.1001/archopht.1991.010800800500261867550 XuYXuDLinSLiuJChengJCheungCAungTWongTSliding window and regression based cup detection in digital fundus images for glaucoma diagnosisMedical Image Computing and Computer-Assisted Intervention–MICCAI2011201118 Acharya, U. Rajendra., Ng, E. Y. K., and Suri, J. S., Image Modeling of the Human Eye, Artech House bioinformatics & biomedical imaging series, Artech House, 2008. OlshausenBAFieldDJSparse coding with an over complete basis set: A strategy employed by V1Vis. Res.199737331133251:STN:280:DyaK1c%2Fos1KrsQ%3D%3D10.1016/S0042-6989(97)00169-79425546 ZhangChangfanChengXiangLiuJianhuaHeJingLiuGuangweiDeep Sparse Autoencoder for Feature Extraction and Diagnosis of Locomotive Adhesion StatusJournal of Control Science and Engineering2018201819 ChrástekRWolfMDonathKNiemannHPaulusDHothornTLausenBLämmerRMardinCYMichelsonGAutomated segmentation of the optic nerve head for diagnosis of glaucomaMed. Image Anal.20059429731410.1016/j.media.2004.12.00415950894 HarizmanNOliveiraCChiangATelloCMarmorMRitchRLiebmannJMThe ISNT rule and differentiation of normal from glaucomatous eyesArch. Ophthalmol.2006124111579158310.1001/archopht.124.11.157917102005 MaheshwariSPachoriRBKanhangadVBhandarySVAcharyaURIterative variational mode decomposition based automated detection of glaucoma using fundus imagesComput. Biol. Med.20178814214910.1016/j.compbiomed.2017.06.01728728059 Eye Diseases Prevalence Research GroupPrevalence of open-angle glaucoma among adults in the United StatesArch. Ophthalmol.200412253253810.1001/archopht.122.4.532 AcharyaURBatSKohJEWBhandarySVAdeliHA novel algorithm to detect glaucoma risk using texton and local configuration pattern features extracted from fundus imagesComput. Biol. Med.201788728310.1016/j.compbiomed.2017.06.02228700902 Chen, Xiangyu, Xu, Yanwu, Wong, Damon Wing Kee, Wong, Tien Yin, and Liu, Jiang, Glaucoma Detection based on Deep Convolutional Neural Network, 37th Annual International IEEE conference on Engineering in Medicine and Biology Society (EMBC), 715–718, 2015. MMR Krishnan (1427_CR16) 2013; 13 Eye Diseases Prevalence Research Group (1427_CR8) 2004; 122 1427_CR11 1427_CR33 1427_CR12 1427_CR31 CF Burgoyne (1427_CR7) 2010 BA Olshausen (1427_CR34) 1997; 37 N Harizman (1427_CR6) 2006; 124 U Raghavendra (1427_CR26) 2018; 441 Y Hagiwara (1427_CR3) 2018; 165 Y Xu (1427_CR27) 2011; 2011 H-C Shin (1427_CR38) 2013; 35 UR Acharya (1427_CR14) 2015; 15 R Chrástek (1427_CR30) 2005; 9 J Yang (1427_CR36) 2015; 26 GK Matsopoulos (1427_CR20) 2008; 32 KP Noronha (1427_CR15) 2014; 10 1427_CR22 S Dua (1427_CR28) 2012; 16 UR Acharya (1427_CR29) 2011; 15 H Goldmann (1427_CR2) 1957; 134 CL Nikias (1427_CR17) 1993 L Chen (1427_CR37) 2018; 428 1427_CR21 1427_CR1 U Raghavendra (1427_CR25) 2017; 38 1427_CR24 S Maheshwari (1427_CR10) 2016; 21 Changfan Zhang (1427_CR35) 2018; 2018 J Nayak (1427_CR18) 2009; 33 S Maheshwari (1427_CR32) 2017; 88 A Sommer (1427_CR4) 1991; 109 MRK Mookiah (1427_CR9) 2012; 33 N Annu (1427_CR13) 2013; 5 HA Quigley (1427_CR5) 2006; 90 UR Acharya (1427_CR23) 2017; 88 R Gayathri (1427_CR19) 2018; 7
References_xml	– reference: Gayathri, R., Rao, P. V., and Aruna, S., Automated glaucoma detection system based on wavelet energy features and ANN. In Advances in Computing, Communications and Informatics (ICACCI, 2014 International Conference on, pp. 2808–2812. IEEE, 2014. – reference: AcharyaURNgEYKEugeneLWJNoronhaKPMinLCNayakKPBhandarySVDecision support system for the glaucoma using Gabor transformationBiomedical Signal Processing and Control201515182610.1016/j.bspc.2014.09.004 – reference: Acharya, U. Rajendra., Ng, E. Y. K., and Suri, J. S., Image Modeling of the Human Eye, Artech House bioinformatics & biomedical imaging series, Artech House, 2008. – reference: SommerATielschJMKatzJQuigleyHAGottschJDJavittJSinghKRelationship between intraocular pressure and primary open angle glaucoma among white and black Americans. The Baltimore eye surveyArch. Ophthalmol.19911098109010951:STN:280:DyaK3MzivVKnsw%3D%3D10.1001/archopht.1991.010800800500261867550 – reference: Simonthomas, S., Thulasi, N., and Asharaf, P., Automated diagnosis of glaucoma using Haralick texture features. In Information Communication and Embedded Systems (ICICES), 2014 International Conference on, pp. 1–6. IEEE, 2014. – reference: NoronhaKPAcharyaURPrabhakar NayakKMartisRJBhandarySVAutomated classification of glaucoma stages using higher order cumulant featuresBiomedical Signal Processing and Control20141017418310.1016/j.bspc.2013.11.006 – reference: ChenLZhouMSuWWuMSheJHirotaKSoftmax regression based deep sparse autoencoder network for facial emotion recognition in human-robot interactionInf. Sci.2018428496110.1016/j.ins.2017.10.044 – reference: Eye Diseases Prevalence Research GroupPrevalence of open-angle glaucoma among adults in the United StatesArch. Ophthalmol.200412253253810.1001/archopht.122.4.532 – reference: ChrástekRWolfMDonathKNiemannHPaulusDHothornTLausenBLämmerRMardinCYMichelsonGAutomated segmentation of the optic nerve head for diagnosis of glaucomaMed. Image Anal.20059429731410.1016/j.media.2004.12.00415950894 – reference: Gajbhiye, Gaurav O., and Kamthane, Ashok N., Automatic classification of glaucomatous images using wavelet and moment feature. In India Conference (INDICON), 2015 Annual IEEE, pp. 1–5. IEEE, 2015. – reference: YangJBaiYLiGLiuMLiuXA novel method of diagnosing premature ventricular contraction based on sparse autoencoder and softmax regressionBiomed. Mater. Eng.201526S1549S155826405919 – reference: RaghavendraUFujitaHBhandarySVGudigarATanJHAcharyaURDeep convolution neural network for accurate diagnosis of glaucoma using digital fundus imagesInf. Sci.2018441414910.1016/j.ins.2018.01.051 – reference: Fink, F., Worle, K., Gruber, P., Tome, A. M., Gorriz-Saez, J. M., Puntonet, C. G., and Lang, E. W., ICA analysis of retina images for glaucoma classification. In Engineering in Medicine and Biology Society, 2008. EMBS 2008. 30th Annual International Conference of the IEEE, pp. 4664–4667. IEEE, 2008. – reference: KrishnanMMRFaustOAutomated glaucoma detection using hybrid feature extraction in retinal fundus imagesJournal of Mechanics in Medicine and Biology20131301135001110.1142/S0219519413500115 – reference: ShinH-COrtonMRCollinsDJDoranSJLeachMOStacked autoencoders for unsupervised feature learning and multiple organ detection in a pilot study using 4D patient dataIEEE Trans. Pattern Anal. Mach. Intell.20133581930194310.1109/TPAMI.2012.27723787345 – reference: DuaSAcharyaURChowriappaPVinitha SreeSWavelet-based energy features for glaucomatous image classificationIEEE Trans. Inf. Technol. Biomed.2012161808710.1109/TITB.2011.217654022113813 – reference: RaghavendraUBhandarySVGudigarAAcharyaURNovel expert system for glaucoma identification using non-parametric spatial envelope energy spectrum with fundus imagesBiocybernetics and Biomedical Engineering201738117018010.1016/j.bbe.2017.11.002 – reference: GoldmannHSchmidtTApplanation tonometryOphthalmologica19571342212421:STN:280:DyaG1c%2Fis12isw%3D%3D10.1159/00030321313484216 – reference: MatsopoulosGKAsvestasPADelibasisKKMouravlianskyNAZeyenTGDetection of glaucomatous change based on vessel shape analysisComput. Med. Imaging Graph.200832318319210.1016/j.compmedimag.2007.11.00318187308 – reference: AcharyaURDuaSDuXSreeSVChuaCKAutomated diagnosis of glaucoma using texture and higher order spectra featuresIEEE Trans Inf Technol Biomed201115344945510.1109/TITB.2011.211932221349793 – reference: MookiahMRKAcharyaURLimCMPetznickASuriJSData mining technique for automated diagnosis of glaucoma using higher order spectra and wavelet energy featuresKnowl.-Based Syst.201233738210.1016/j.knosys.2012.02.010 – reference: AcharyaURBatSKohJEWBhandarySVAdeliHA novel algorithm to detect glaucoma risk using texton and local configuration pattern features extracted from fundus imagesComput. Biol. Med.201788728310.1016/j.compbiomed.2017.06.02228700902 – reference: Yin, Fengshou, Liu, Jiang, Wong, Damon Wing Kee, Tan, Ngan Meng, Cheung, Carol, Baskaran, Mani, Aung, Tin, and Wong, Tien Yin., Automated segmentation of optic disc and optic cup in fundus images for glaucoma diagnosis. In Computer-based medical systems (CBMS), 2012 25th international symposium on, pp. 1–6. IEEE, 2012. – reference: HagiwaraYKohJEWTanJHBhandarySVLaudeACiaccioEJTongLAcharyaURComputer-aided diagnosis of glaucoma using fundus images: A reviewComput. Methods Prog. Biomed.201816511210.1016/j.cmpb.2018.07.012 – reference: OlshausenBAFieldDJSparse coding with an over complete basis set: A strategy employed by V1Vis. Res.199737331133251:STN:280:DyaK1c%2Fos1KrsQ%3D%3D10.1016/S0042-6989(97)00169-79425546 – reference: HarizmanNOliveiraCChiangATelloCMarmorMRitchRLiebmannJMThe ISNT rule and differentiation of normal from glaucomatous eyesArch. Ophthalmol.2006124111579158310.1001/archopht.124.11.157917102005 – reference: MaheshwariSPachoriRBAcharyaURAutomated diagnosis of glaucoma using empirical wavelet transform and correntropy features extracted from fundus imagesIEEE journal of biomedical and health informatics201621380381310.1109/JBHI.2016.254496128113877 – reference: Chen, Xiangyu, Xu, Yanwu, Wong, Damon Wing Kee, Wong, Tien Yin, and Liu, Jiang, Glaucoma Detection based on Deep Convolutional Neural Network, 37th Annual International IEEE conference on Engineering in Medicine and Biology Society (EMBC), 715–718, 2015. – reference: QuigleyHABromanATThe number of people with glaucoma worldwide in 2010 and 2020Br. J. Ophthalmol.20069032622671:STN:280:DC%2BD28%2FpvFOjsA%3D%3D10.1136/bjo.2005.081224164889401856963 – reference: ZhangChangfanChengXiangLiuJianhuaHeJingLiuGuangweiDeep Sparse Autoencoder for Feature Extraction and Diagnosis of Locomotive Adhesion StatusJournal of Control Science and Engineering2018201819 – reference: NayakJAcharyaRSubbanna BhatPShettyNLimT-CAutomated diagnosis of glaucoma using digital fundus imagesJ. Med. Syst.200933533710.1007/s10916-008-9195-z19827259 – reference: Maheshwari, S., Kanhangad, V., Pachori, R. B., Bhandary, S. V., and Acharya, U. R., Automated glaucoma diagnosis using bit-plane slicing and local binary pattern techniques. Comput. Biol. Med., 2018 (In Press). – reference: BurgoyneCFPearls of glaucoma management2010Berlin, HeidelbergSpringer(Chapter 1. p. 1) – reference: NikiasCLPetropuluAPHigher-order spectra analysis: A nonlinear signal processing framework1993Englewood Cliffs, NJPTR Prentice Hall – reference: GayathriRRaoPVGlaucoma detection using cup to disc ratio and artificial neural networksInternational Journal of Engineering & Technology20187135140 – reference: XuYXuDLinSLiuJChengJCheungCAungTWongTSliding window and regression based cup detection in digital fundus images for glaucoma diagnosisMedical Image Computing and Computer-Assisted Intervention–MICCAI2011201118 – reference: MaheshwariSPachoriRBKanhangadVBhandarySVAcharyaURIterative variational mode decomposition based automated detection of glaucoma using fundus imagesComput. Biol. Med.20178814214910.1016/j.compbiomed.2017.06.01728728059 – reference: AnnuNJustinJAutomated classification of glaucoma images by wavelet energy featuresInternational Journal of Engineering and Technology20135217161721 – volume: 10 start-page: 174 year: 2014 ident: 1427_CR15 publication-title: Biomedical Signal Processing and Control doi: 10.1016/j.bspc.2013.11.006 – volume: 5 start-page: 1716 issue: 2 year: 2013 ident: 1427_CR13 publication-title: International Journal of Engineering and Technology – volume: 21 start-page: 803 issue: 3 year: 2016 ident: 1427_CR10 publication-title: IEEE journal of biomedical and health informatics doi: 10.1109/JBHI.2016.2544961 – ident: 1427_CR12 doi: 10.1109/INDICON.2015.7443150 – volume: 165 start-page: 1 year: 2018 ident: 1427_CR3 publication-title: Comput. Methods Prog. Biomed. doi: 10.1016/j.cmpb.2018.07.012 – ident: 1427_CR21 doi: 10.1109/ICICES.2014.7033743 – volume: 134 start-page: 221 year: 1957 ident: 1427_CR2 publication-title: Ophthalmologica doi: 10.1159/000303213 – volume: 109 start-page: 1090 issue: 8 year: 1991 ident: 1427_CR4 publication-title: Arch. Ophthalmol. doi: 10.1001/archopht.1991.01080080050026 – volume: 38 start-page: 170 issue: 1 year: 2017 ident: 1427_CR25 publication-title: Biocybernetics and Biomedical Engineering doi: 10.1016/j.bbe.2017.11.002 – ident: 1427_CR31 doi: 10.1109/CBMS.2012.6266344 – ident: 1427_CR22 – volume: 428 start-page: 49 year: 2018 ident: 1427_CR37 publication-title: Inf. Sci. doi: 10.1016/j.ins.2017.10.044 – ident: 1427_CR24 doi: 10.1109/EMBC.2015.7318462 – volume: 88 start-page: 142 year: 2017 ident: 1427_CR32 publication-title: Comput. Biol. Med. doi: 10.1016/j.compbiomed.2017.06.017 – volume: 9 start-page: 297 issue: 4 year: 2005 ident: 1427_CR30 publication-title: Med. Image Anal. doi: 10.1016/j.media.2004.12.004 – ident: 1427_CR1 – volume: 124 start-page: 1579 issue: 11 year: 2006 ident: 1427_CR6 publication-title: Arch. Ophthalmol. doi: 10.1001/archopht.124.11.1579 – ident: 1427_CR11 – volume: 32 start-page: 183 issue: 3 year: 2008 ident: 1427_CR20 publication-title: Comput. Med. Imaging Graph. doi: 10.1016/j.compmedimag.2007.11.003 – volume: 37 start-page: 3311 year: 1997 ident: 1427_CR34 publication-title: Vis. Res. doi: 10.1016/S0042-6989(97)00169-7 – volume-title: Higher-order spectra analysis: A nonlinear signal processing framework year: 1993 ident: 1427_CR17 – volume: 13 start-page: 1350011 issue: 01 year: 2013 ident: 1427_CR16 publication-title: Journal of Mechanics in Medicine and Biology doi: 10.1142/S0219519413500115 – volume: 16 start-page: 80 issue: 1 year: 2012 ident: 1427_CR28 publication-title: IEEE Trans. Inf. Technol. Biomed. doi: 10.1109/TITB.2011.2176540 – ident: 1427_CR33 doi: 10.1016/j.compbiomed.2018.11.028 – volume: 88 start-page: 72 year: 2017 ident: 1427_CR23 publication-title: Comput. Biol. Med. doi: 10.1016/j.compbiomed.2017.06.022 – volume: 26 start-page: S1549 year: 2015 ident: 1427_CR36 publication-title: Biomed. Mater. Eng. – volume: 2011 start-page: 1 year: 2011 ident: 1427_CR27 publication-title: Medical Image Computing and Computer-Assisted Intervention–MICCAI – volume: 33 start-page: 337 issue: 5 year: 2009 ident: 1427_CR18 publication-title: J. Med. Syst. doi: 10.1007/s10916-008-9195-z – volume: 90 start-page: 262 issue: 3 year: 2006 ident: 1427_CR5 publication-title: Br. J. Ophthalmol. doi: 10.1136/bjo.2005.081224 – volume: 35 start-page: 1930 issue: 8 year: 2013 ident: 1427_CR38 publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2012.277 – volume-title: Pearls of glaucoma management year: 2010 ident: 1427_CR7 – volume: 33 start-page: 73 year: 2012 ident: 1427_CR9 publication-title: Knowl.-Based Syst. doi: 10.1016/j.knosys.2012.02.010 – volume: 441 start-page: 41 year: 2018 ident: 1427_CR26 publication-title: Inf. Sci. doi: 10.1016/j.ins.2018.01.051 – volume: 122 start-page: 532 year: 2004 ident: 1427_CR8 publication-title: Arch. Ophthalmol. doi: 10.1001/archopht.122.4.532 – volume: 2018 start-page: 1 year: 2018 ident: 1427_CR35 publication-title: Journal of Control Science and Engineering doi: 10.1155/2018/8676387 – volume: 15 start-page: 18 year: 2015 ident: 1427_CR14 publication-title: Biomedical Signal Processing and Control doi: 10.1016/j.bspc.2014.09.004 – volume: 7 start-page: 135 year: 2018 ident: 1427_CR19 publication-title: International Journal of Engineering & Technology – volume: 15 start-page: 449 issue: 3 year: 2011 ident: 1427_CR29 publication-title: IEEE Trans Inf Technol Biomed doi: 10.1109/TITB.2011.2119322
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SubjectTerms	Adult Algorithms Case-Control Studies Diagnostic Techniques, Ophthalmological Digital imaging Female Fundus Oculi Glaucoma Health Informatics Health Informatics and Computer Vision Health Sciences Humans Image & Signal Processing Image Interpretation, Computer-Assisted - methods Intraocular pressure Learning algorithms Machine learning Male Medicine Medicine & Public Health Middle Aged Pattern Recognition, Automated - methods Quantitative analysis Recent Advances in Deep Learning for Biomedical Signal Processing Statistics for Life Sciences Test procedures Tomography, Optical Coherence - methods Vision
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Title	A Two Layer Sparse Autoencoder for Glaucoma Identification with Fundus Images
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