Deep learning based classification network for diagnosis of glaucoma in two dimensional retinal fundus images
Keywords:
glaucoma, CAD, deep feed, neural network, dimensional retinal fundus imagesAbstract
Glaucoma is an eye condition which prompts lasting visual deficiency when the illness advances to a propelled stage. It happens because of high intraocular pressure inside the eye, bringing about harm to the optic nerve. Glaucoma doesn't show any side effects in its initial stage and in this manner, it is critical to analyze ahead of schedule to forestall visual impairment. Fundus photography is broadly utilized by ophthalmologists to aid analysis of glaucoma and is cost-effective. The utilization of Computer Aided Diagnosis(CAD) is successful in the conclusion of glaucoma and can help the clinicians to reduce their remaining task at hand altogether. We have likewise talked about the upsides of utilizing condition of-workmanship procedures, including Deep learning (DL), when building up the robotized framework. The DL techniques are powerful in glaucoma diagnosis. Novel DL calculations with large information accessibility are required to build up a dependable CAD framework. Such procedures can be utilized to analyze other eye diseases precisely. This paper examines the helpfulness of CAD frameworks and deep feed forward neural network to fairly analyze patients with glaucoma. Additionally, unique AI strategies utilized by scientists over the past decade are summarized.
Downloads
References
Lim, T. C., Chattopadhyay, S., & Acharya, U. R. A survey and comparative study on the instruments for glaucoma detection. Medical engineering & physics, 34(2), 129-139. (2012).
Dua, S., Acharya, U. R., Chowriappa, P., & Sree, S. V. Wavelet-based energy features for glaucomatous image classification. Ieee transactions on information technology in biomedicine, 16(1), 80-87. (2011).
Lin, J. C. H., Zhao, Y., Chen, P. J., Humayun, M., & Tai, Y. C. Feeling the pressure: A parylene-based intraocular pressure sensor. IEEE Nanotechnology Magazine, 6(3), 8-16. (2012).
Cheng, J., Liu, J., Xu, Y., Yin, F., Wong, D.W.K., Tan, N.M., Tao, D., Cheng, C.Y., Aung, T. and Wong, T.Y.. Superpixel classification based optic disc and optic cup segmentation for glaucoma screening. IEEE transactions on medical imaging, 32(6), 1019-1032. (2013)
Nayak, J., Acharya, R., Bhat, P. S., Shetty, N., & Lim, T. C. Automated diagnosis of glaucoma using digital fundus images. Journal of medical systems, 33(5), 337. (2009).
Lee, S. D., Lee, J. H., Choi, Y. G., You, H. C., Kang, J. H., & Jun, C. H. Machine learning models based on the dimensionality reduction of standard automated perimetry data for glaucoma diagnosis. Artificial intelligence in medicine, 94, 110-116. (2019).
Jammal, A.A., Thompson, A.C., Mariottoni, E.B., Berchuck, S.I., Urata, C.N., Estrela, T., Wakil, S.M., Costa, V.P. and Medeiros, F.A., Human versus machine: comparing a deep learning algorithm to human gradings for detecting glaucoma on fundus photographs. American Journal of Ophthalmology, 211, pp.123-131. (2020)
Baxter, S. L., Marks, C., Kuo, T. T., Ohno-Machado, L., & Weinreb, R. N. Machine learning-based predictive modeling of surgical intervention in glaucoma using systemic data from electronic health records. American Journal of Ophthalmology, 208, 30-40. (2019).
Yousefi, S., Kiwaki, T., Zheng, Y., Sugiura, H., Asaoka, R., Murata, H., Lemij, H. and Yamanishi, K.,. Detection of longitudinal visual field progression in glaucoma using machine learning. American journal of ophthalmology, 193, pp.71-79.( 2018)
Wang, X., Chen, H., Ran, A.R., Luo, L., Chan, P.P., Tham, C.C., Chang, R.T., Mannil, S.S., Cheung, C.Y. and Heng, P.A.,. Towards multi-center glaucoma OCT image screening with semi-supervised joint structure and function multi-task learning. Medical Image Analysis, 63, 101695. (2020)
Claro, M., Veras, R., Santana, A., Araújo, F., Silva, R., Almeida, J., & Leite, D. An hybrid feature space from texture information and transfer learning for glaucoma classification. Journal of Visual Communication and Image Representation, 64, 102597. (2019).
Al-Akhras, M., Alawairdhi, M., & Habib, M.. Using soft computing techniques to diagnose Glaucoma disease. Journal of Infection and Public Health. PP.8 (2019)
Bisneto, T. R. V., de Carvalho Filho, A. O., & Magalhães, D. M. V. Generative adversarial network and texture features applied to automatic glaucoma detection. Applied Soft Computing, 90, 106165. (2020).
Ma, Y., Amini, N., & Ghasemzadeh, H. (2016). Wearable sensors for gait pattern examination in glaucoma patients. Microprocessors and Microsystems, 46, 67-74.
Martins, J., Cardoso, J. S., & Soares, F. (2020). Offline computer-aided diagnosis for Glaucoma detection using fundus images targeted at mobile devices. Computer Methods and Programs in Biomedicine, 192, 105341.
Orlando, J.I., Fu, H., Breda, J.B., van Keer, K., Bathula, D.R., Diaz-Pinto, A., Fang, R., Heng, P.A., Kim, J., Lee, J. and Lee, J., 2020. Refuge challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs. Medical image analysis, 59, p.101570.
Kishore, B., & Ananthamoorthy, N. P. (2020). Glaucoma classification based on intra-class and extra-class discriminative correlation and consensus ensemble classifier. Genomics.
Phene, S., Dunn, R.C., Hammel, N., Liu, Y., Krause, J., Kitade, N., Schaekermann, M., Sayres, R., Wu, D.J., Bora, A. and Semturs, C., Deep learning and glaucoma specialists: the relative importance of optic disc features to predict glaucoma referral in fundus photographs. Ophthalmology, 126(12), pp.1627-1639.(2019)
Hu, R., Racette, L., Chen, K. S., & Johnson, C. A. FUNCTIONAL ASSESSMENT OF GLAUCOMA PROGRESSION: UNCOVERING PROGRESSION. Survey of Ophthalmology , (2020).
Gour, N., & Khanna, P. Automated glaucoma detection using GIST and pyramid histogram of oriented gradients (PHOG) descriptors. Pattern Recognition Letters. (2019).
Maheshwari, S., Kanhangad, V., Pachori, R. B., Bhandary, S. V., & Acharya, U. R. Automated glaucoma diagnosis using bit-plane slicing and local binary pattern techniques. Computers in Biology and Medicine, 105, 72-80. (2019).
Chan, Y. M., Ng, E. Y. K., Jahmunah, V., Koh, J. E. W., Lih, O. S., Leon, L. Y. W., & Acharya, U. R. (2019). Automated detection of glaucoma using optical coherence tomography angiogram images. Computers in biology and medicine, 115, 103483.
Raghavendra, U., Fujita, H., Bhandary, S. V., Gudigar, A., Tan, J. H., & Acharya, U. R. Deep convolution neural network for accurate diagnosis of glaucoma using digital fundus images. Information Sciences, 441, 41-49. (2018).
Sarhan, A., Rokne, J., & Alhajj, R. (2019). Glaucoma detection using image processing techniques: A literature review. Computerized Medical Imaging and Graphics, 78, 101657. (2019).
Asaoka, R., Murata, H., Iwase, A., & Araie, M. (2016). Detecting preperimetric glaucoma with standard automated perimetry using a deep learning classifier. Ophthalmology, 123(9), 1974-1980.
Mohamed, N. A., Zulkifley, M. A., Zaki, W. M. D. W., & Hussain, A. An automated glaucoma screening system using cup-to-disc ratio via Simple Linear Iterative Clustering superpixel approach. Biomedical Signal Processing and Control, 53, 101454. (2019).
Mvoulana, A., Kachouri, R., & Akil, M. (2019). Fully automated method for glaucoma screening using robust optic nerve head detection and unsupervised segmentation based cup-to-disc ratio computation in retinal fundus images. Computerized Medical Imaging and Graphics, 77, 101643.
dos Santos Ferreira, M. V., de Carvalho Filho, A. O., de Sousa, A. D., Silva, A. C., & Gattass, M. Convolutional neural network and texture descriptor-based automatic detection and diagnosis of glaucoma. Expert Systems with Applications, 110, 250-263. (2018).
Raghavendra, U., Bhandary, S. V., Gudigar, A., & Acharya, U. R. Novel expert system for glaucoma identification using non-parametric spatial envelope energy spectrum with fundus images. Biocybernetics and Biomedical Engineering, 38(1), 170-180. (2018).
Maheshwari, S., Pachori, R. B., Kanhangad, V., Bhandary, S. V., & Acharya, U. R. Iterative variational mode decomposition based automated detection of glaucoma using fundus images. Computers in biology and medicine, 88, 142-149. (2017).
Kausu, T. R., Gopi, V. P., Wahid, K. A., Doma, W., & Niwas, S. I. Combination of clinical and multiresolution features for glaucoma detection and its classification using fundus images. Biocybernetics and Biomedical Engineering, 38(2), 329-341. (2018).
Bowd, C., Belghith, A., Proudfoot, J. A., Zangwill, L. M., Christopher, M., Goldbaum, M. H., ... & Weinreb, R. N. (2020). Gradient Bosting Classifiers Combining Vessel Density and Tissue Thickness Measurements for Classifying Early to Moderate Glaucoma. American Journal of Ophthalmology
Santhi, D., Manimegalai, D., Parvathi, S., & Karkuzhali, S. (2016). Segmentation and classification of bright lesions to diagnose diabetic retinopathy in retinal images. Biomedical Engineering/Biomedizinische Technik, 61(4), 443-453.
Karkuzhali, S., & Manimegalai, D. (2017). Computational intelligence-based decision support system for glaucoma detection.
Santhi, D., Manimegalai, D., & Karkuzhali, S. (2014). Diagnosis of diabetic retinopathy by exudates detection using clustering techniques. Biomedical Engineering: Applications, Basis and Communications, 26(06), 1450077.
Karkuzhali, S., & Manimegalai, D. (2018). Retinal haemorrhages segmentation using improved toboggan segmentation algorithm in diabetic retinopathy images.
Karkuzhali, S., & Manimegalai, D. (2018). Detection of hemorrhages in retinal images using hybrid approach for diagnosis of diabetic retinopathy. International Journal of Pure and Applied Mathematics, 118(18), 2841-2846.
Karkuzhali, S., & Manimegalai, D. (2019). Robust intensity variation and inverse surface adaptive thresholding techniques for detection of optic disc and exudates in retinal fundus images. Biocybernetics and Biomedical Engineering, 39(3), 753-764.
Suriyasekeran, K., Santhanamahalingam, S., & Duraisamy, M. (2020). Algorithms for Diagnosis of Diabetic Retinopathy and Diabetic Macula Edema-A Review.
Karkuzhali, S., & Manimegalai, D. (2019). Distinguising Proof of Diabetic Retinopathy Detection by Hybrid Approaches in Two Dimensional Retinal Fundus Images. Journal of Medical Systems, 43(6), 173.
Karkuzhali, S., & Manimegalai, D. (2018). Microanaurysms Identification using Computational Intelligence approach in Two Dimensional Fundus Images for detection of Diabetic Retinopathy. International Journal of Pure and Applied Mathematics, 118(8), 485-491.
Thakur, N., & Juneja, M. (2018). Survey on segmentation and classification approaches of optic cup and optic disc for diagnosis of glaucoma. Biomedical Signal Processing and Control, 42, 162-189.
Zilly, J., Buhmann, J. M., & Mahapatra, D. (2017). Glaucoma detection using entropy sampling and ensemble learning for automatic optic cup and disc segmentation. Computerized Medical Imaging and Graphics, 55, 28-41.
Divya, L., & Jacob, J. (2018). Performance analysis of Glaucoma detection approaches from fundus images. Procedia computer science, 143, 544-551
de Carvalho Junior, A. S. V., Carvalho, E. D., de Carvalho Filho, A. O., de Sousa, A. D., Silva, A. C., & Gattass, M. (2018). Automatic methods for diagnosis of glaucoma using texture descriptors based on phylogenetic diversity. Computers & Electrical Engineering, 71, 102-114.
Singh, A., Dutta, M. K., ParthaSarathi, M., Uher, V., & Burget, R. (2016). Image processing based automatic diagnosis of glaucoma using wavelet features of segmented optic disc from fundus image. Computer methods and programs in biomedicine, 124, 108-120.
Boquete, L., Miguel-Jiménez, J. M., Ortega, S., Rodríguez-Ascariz, J. M., Pérez-Rico, C., & Blanco, R. (2012). Multifocal electroretinogram diagnosis of glaucoma applying neural networks and structural pattern analysis. Expert Systems with Applications, 39(1), 234-238.
Acharya, U. R., Bhat, S., Koh, J. E., Bhandary, S. V., & Adeli, H. (2017). A novel algorithm to detect glaucoma risk using texton and local configuration pattern features extracted from fundus images. Computers in biology and medicine, 88, 72-83.
Mitra, A., Banerjee, P. S., Roy, S., Roy, S., & Setua, S. K. (2018). The region of interest localization for glaucoma analysis from retinal fundus image using deep learning. Computer methods and programs in biomedicine, 165, 25-35.
Issac, A., Sarathi, M. P., & Dutta, M. K. (2015). An adaptive threshold based image processing technique for improved glaucoma detection and classification. Computer methods and programs in biomedicine, 122(2), 229-244.
Miri M., S., Abràmoff, M. D., Kwon, Y. H., Sonka, M., & Garvin, M. K. (2017). A machine-learning graph-based approach for 3D segmentation of Bruch’s membrane opening from glaucomatous SD-OCT volumes. Medical image analysis, 39, 206-217.
Medeiros, F. A., Jammal, A. A., & Thompson, A. C. (2019). From machine to machine: an OCT-trained deep learning algorithm for objective quantification of glaucomatous damage in fundus photographs. Ophthalmology, 126(4), 513-521.
Meriaudeau, F. (2018). Machine learning and deep learning approaches for retinal disease diagnosis. 3rd International Conference on Computer Science and Computational Intelligence 2018, Procedia Computer Science 135 (2018) 2
Mookiah, M. R. K., Acharya, U. R., Lim, C. M., Petznick, A., & Suri, J. S. (2012). Data mining technique for automated diagnosis of glaucoma using higher order spectra and wavelet energy features. Knowledge-Based Systems, 33, 73-82.
Niwas, S.I., Jakhetiya, V., Lin, W., Kwoh, C.K., Sng, C.C., Aquino, M.C., Victor, K. and Chew, P.T., 2016. Complex wavelet based quality assessment for AS-OCT images with application to Angle Closure Glaucoma diagnosis. Computer methods and programs in biomedicine, 130, pp.13-21.
Niwas, S.I., Lin, W., Bai, X., Kwoh, C.K., Kuo, C.C.J., Sng, C.C., Aquino, M.C. and Chew, P.T., 2016. Automated anterior segment OCT image analysis for Angle Closure Glaucoma mechanisms classification. Computer methods and programs in biomedicine, 130, pp.65-75.
Asaoka, R., Hirasawa, K., Iwase, A., Fujino, Y., Murata, H., Shoji, N., & Araie, M. (2017). Validating the usefulness of the “random forests” classifier to diagnose early glaucoma with optical coherence tomography. American journal of ophthalmology, 174, 95-103.
Cheng, L., Ding, Y., Hao, K., & Hu, Y. (2012). An ensemble kernel classifier with immune clonal selection algorithm for automatic discriminant of primary open-angle glaucoma. Neurocomputing, 83, 1-11.
Acharya, U. R., Ng, E. Y. K., Eugene, L. W. J., Noronha, K. P., Min, L. C., Nayak, K. P., & Bhandary, S. V. (2015). Decision support system for the glaucoma using Gabor transformation. Biomedical Signal Processing and Control, 15, 18-26.
Rehman, Z. U., Naqvi, S. S., Khan, T. M., Arsalan, M., Khan, M. A., & Khalil, M. A. (2019). Multi-parametric optic disc segmentation using superpixel based feature classification. Expert Systems with Applications, 120, 461-473.
Suryasa, I. W., Rodríguez-Gámez, M., & Koldoris, T. (2021). Health and treatment of diabetes mellitus. International Journal of Health Sciences, 5(1), i-v. https://doi.org/10.53730/ijhs.v5n1.2864
Gaibullaeva, N. N. (2021). The role of clinical examination early diagnosis of glaucoma. International Journal of Health & Medical Sciences, 4(3), 333-337. https://doi.org/10.31295/ijhms.v4n3.1745
Published
How to Cite
Issue
Section
Copyright (c) 2022 International journal of health sciences
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Articles published in the International Journal of Health Sciences (IJHS) are available under Creative Commons Attribution Non-Commercial No Derivatives Licence (CC BY-NC-ND 4.0). Authors retain copyright in their work and grant IJHS right of first publication under CC BY-NC-ND 4.0. Users have the right to read, download, copy, distribute, print, search, or link to the full texts of articles in this journal, and to use them for any other lawful purpose.
Articles published in IJHS can be copied, communicated and shared in their published form for non-commercial purposes provided full attribution is given to the author and the journal. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
This copyright notice applies to articles published in IJHS volumes 4 onwards. Please read about the copyright notices for previous volumes under Journal History.
