Automated Diabetic Retinopathy Grading based on the Modified Capsule Network Architecture

REFERENCES

• World Health Organization. Global status report on noncommunicable diseases 2014. No. WHO/NMH/NVI/15.1. World Health Organization, 2014.
   Google Scholar
• X. Lin, et al., “Global, regional, and national burden and trend of diabetes in 195 countries and territories: An analysis from 1990 to 2025,” Sci. Rep., Vol. 10, no. 1, pp. 1–11, 2020. DOI: 10.1038/s41598-020-71908-9
   PubMed Web of Science ®Google Scholar
• Y. Niu, et al., “Pathological evidence exploration in deep retinal image diagnosis,” Proc. AAAI Conf. Artif. Intell., Vol. 33, pp. 1093–101, 2019. DOI: 10.1609/aaai.v33i01.33011093
   Google Scholar
• R. Raman, S. Srinivasan, S. Virmani, S. Sivaprasad, C. Rao and R. Rajalakshmi, “Fundus photograph-based deep learning algorithms in detecting diabetic retinopathy,” Eye, Vol. 33, pp. 97–109, 2019. DOI: 10.1038/s41433-018-0269-y
   PubMed Web of Science ®Google Scholar
• K. B. Khan, et al., “A review of retinal blood vessels extraction techniques: Challenges, taxonomy, and future trends,” Pattern Anal. Appl., Vol. 22, no. 3, pp. 767–802, 2019. DOI: 10.1007/s10044-018-0754-8
   Web of Science ®Google Scholar
• J. Kaur, D. Mittal and R. Singla, “Diabetic retinopathy diagnosis through computer-aided fundus image analysis: A review,” Arch. Comput. Methods Eng., Vol. 29, pp. 1673–711, 2021. DOI: 10.1007/s11831-021-09635-1
   Web of Science ®Google Scholar
• N. Tandon, et al., “The increasing burden of diabetes and variations among the states of India: The global burden of disease study 1990–2016,” Lancet Global Health, Vol. 6, no. 12, pp. e1352–62, 2018. DOI: 10.1016/S2214-109X(18)30387-5
   PubMed Web of Science ®Google Scholar
• N. C. Sharma. A government survey found an 11.8% prevalence of diabetes in India. Livemint, 2019.
   Google Scholar
• P. I. Burgess, I. J. C. MacCormick, S. P. Harding, A. Bastawrous, N. A. V. Beare and P. Garner, “Epidemiology of diabetic retinopathy and maculopathy in Africa: A systematic review,” Diabetic Med., Vol. 30, no. 4, pp. 399–412, 2013. DOI: 10.1111/j.1464-5491.2012.03756.x
   PubMed Web of Science ®Google Scholar
• S. Atre, “The burden of diabetes in India,” Lancet Global Health, Vol. 7, no. 4, pp. e418, 2019. DOI: 10.1016/S2214-109X(18)30556-4
   PubMed Web of Science ®Google Scholar
• T. Peto and C. Tadros, “Screening for diabetic retinopathy and diabetic macular edema in the United Kingdom,” Curr. Diabetes Rep., Vol. 12, no. 4, pp. 338–45, 2012. DOI: 10.1007/s11892-012-0285-4
   PubMed Web of Science ®Google Scholar
• M. R. K. Mookiah, U. Rajendra Acharya, C. K. Chua, C. M. Lim, E. Y. K. Ng and A. Laude, “Computer-aided diagnosis of diabetic retinopathy: A review,” Comput. Biol. Med., Vol. 43, no. 12, pp. 2136–55, 2013. DOI: 10.1016/j.compbiomed.2013.10.007
   PubMed Web of Science ®Google Scholar
• V. Gulshan, et al., “Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs,” JAMA, Vol. 316, no. 22, pp. 2402–10, 2016. DOI: 10.1001/jama.2016.17216
   PubMed Web of Science ®Google Scholar
• P. Liskowski and K. O. Krawiec, “Segmenting retinal blood vessels with deep neural networks,” IEEE Trans. Med. Imaging, Vol. 35, no. 11, pp. 2369–80, 2016. DOI: 10.1109/TMI.2016.2546227
   PubMed Web of Science ®Google Scholar
• S. Wang, Y. Yin, G. Cao, B.-z. Wei, Y. Zheng and G. Yang, “Hierarchical retinal blood vessel segmentation based on feature and ensemble learning,” Neurocomputing, Vol. 149, pp. 708–17, 2015. DOI: 10.1016/j.neucom.2014.07.059
   Web of Science ®Google Scholar
• M. H. Ahmad Fadzil, L. I. Izhar, H. Nu-groho and H. A. Nugroho, “Analysis of retinal fundus images for grading of diabetic retinopathy severity,” Med. Biol. Eng. Comput., Vol. 49, no. 6, pp. 693–700, 2011. DOI: 10.1007/s11517-011-0734-2
   PubMed Web of Science ®Google Scholar
• S. Jalan and A. A. Tayade, “Review paper on diagnosis of diabetic retinopathy using KNN and SVM algorithms,” Int. J. Adv. Res. Comput. Sci. Manage. Stud., Vol. 3, no. 1, pp. 128–31, 2015.
   Google Scholar
• A. Ali, et al., “Machine learning based automated segmentation and hybrid feature analysis for diabetic retinopathy classification using fundus image,” Entropy, Vol. 22, no. 5, pp. 567, 2020. DOI: 10.3390/e22050567
   PubMed Web of Science ®Google Scholar
• T. Vijayan, M. Sangeetha, A. Kumaravel and B. Karthik, “Gabor filter and machine learning-based diabetic retinopathy analysis and detection,” Microprocess. Microsyst., p. 103353, 2020. DOI: 10.3390/s22145103
   Google Scholar
• N. Silberman, K. Ahrlich, R. Fergus and L. N. Subramanian, “The case for automated detection of diabetic retinopathy,” in 2010 AAAI Spring Symposium Series, Palo Alto, California, March 22–24, 2010.
   Google Scholar
• D. Wu, M. Zhang, J.-C. Liu and W. a. Bauman, “On the adaptive detection of blood vessels in retinal images,” IEEE Trans. Biomed. Eng., Vol. 53, no. 2, pp. 341–3, 2006. DOI: 10.1109/TBME.2005.862571
   PubMed Web of Science ®Google Scholar
• P. K. Chaudhary and R. B. Pachori, “Automatic diagnosis of different grades of diabetic retinopathy and diabetic macular edema using 2-D-FBSE-FAWT,” IEEE Trans. Instrum. Meas., Vol. 71, pp. 1–9, 2022. DOI: 10.1109/TIM.2022.3140437
   Web of Science ®Google Scholar
• H. Jang, D. McCormack and F. Tong, “Evaluating the robustness of object recognition to visual noise in humans and convolutional neural networks,” J. Vision, Vol. 17, no. 10, p. 805, 2017. DOI: 10.1167/17.10.805
   Google Scholar
• R. Gargeya and T. Leng, “Automated identification of diabetic retinopathy using deep learning,” Ophthalmology, Vol. 124, no. 7, pp. 962–9, 2017. DOI: 10.1016/j.ophtha.2017.02.008
   PubMed Web of Science ®Google Scholar
• C. Lam, D. Yi, M. Guo and T. Lindsey, “Automated detection of diabetic retinopathy using deep learning,” AMIA Jt. Summits Transl. Sci. Proc., Vol. 2018, pp. 147–55, 2018.
   Google Scholar
• B. Tymchenko, P. Marchenko and D. Spodarets, “Deep learning approach to diabetic retinopathy detection,” arXiv preprint arXiv:2003.02261, 2020.
   Google Scholar
• F. Huang, B. Dashtbozorg, J. Zhang, E. Bekkers, S. Abbasi-Sureshjani, T. T. Berendschot and B. M. H. Romeny, “Reliability of using retinal vascular fractal dimension as a biomarker in the diabetic retinopathy detection,” J. Ophthalmol., 2016, p. 6259047, 2016. DOI: 10.1155/2016/6259047
   PubMed Web of Science ®Google Scholar
• G. Leontidis, B. Al-Diri and A. Hunter, “A new unified framework for the early detection of the progression to diabetic retinopathy from fundus images,” Comput. Biol. Med., Vol. 90, pp. 98–115, 2017. DOI: 10.1016/j.compbiomed.2017.09.008
   PubMed Web of Science ®Google Scholar
• O. Perdomo, H. Rios, F. J. Rodrıguez, S. Otalora, F. Meriaudeau, H. Muller and F. A. Gonzalez, “Classification of diabetes-related retinal diseases using a deep learning approach in optical coherence tomography,” Comput. Methods Program. Biomed., Vol. 178, pp. 181–9, 2019. DOI: 10.1016/j.cmpb.2019.06.016
   PubMed Web of Science ®Google Scholar
• H. Pratt, F. Coenen, D. M. Broadbent, S.-m. P. Harding and Y. Zheng, “Convolutional neural networks for diabetic retinopathy,” Procedia. Comput. Sci., Vol. 90, pp. 200–5, 2016. DOI: 10.1016/j.procs.2016.07.014
   Google Scholar
• A. Kori, S. S. Chennam Setty, M. Safwan K.P. and V. Alex, “Ensemble of convolutional neural networks for automatic grading of diabetic retinopathy and macular edema,” arXiv preprint arXiv:1809.04228, 2018.
   Google Scholar
• J. de la Torre, A. Valls and D. Puig, “A deep learning interpretable classifier for diabetic retinopathy disease grading,” Neurocomputing, Vol. 396, pp. 465–76, 2020. DOI: 10.1016/j.neucom.2018.07.102
   Web of Science ®Google Scholar
• P. Costa, T. Araujo, G. Aresta, A. Galdran, A. M. Mendonca, A. Smailagic and A. Campilho, “EyeWeS: Weakly supervised pre-trained convolutional neural networks for diabetic retinopathy detection,” in 2019 16th International Conference on Machine Vision Applications (MVA), Tokyo, Japan: IEEE, 2019, pp. 1–6. DOI: 10.23919/MVA.2019.8757991
   Google Scholar
• O. Daanouni, B. Cherradi and A. Tmiri, “The self-attention mechanism for diabetic retinopathy detection,” in Emerging Trends in ICT for Sustainable Development: The Proceedings of NICE2020 International Conference, Springer International Publishing, 2021, pp. 79–88.
   Google Scholar
• Y.-T. Hsieh, et al., “Application of deep learning image assessment software Verisee™ for diabetic retinopathy screening,” J. Formosan Med. Assoc., Vol. 120, no. 1, pp. 165–71, 2021. DOI: 10.1016/j.jfma.2020.03.024
   PubMed Web of Science ®Google Scholar
• M. Mateen, J. Wen, N. Nasrul-lah, S. Sun and S. Hayat, “Exudate detection for diabetic retinopathy using pretrained convolutional neural networks,” Complexity, Vol. 2020, pp. 1–11, 2020. DOI: 10.1155/2020/5801870
   Web of Science ®Google Scholar
• S. M. Roshan, A. Karsaz, A. H. Ve-jdani and Y. M. Roshan, “Fine-tuning of pre-trained convolutional neural networks for diabetic retinopathy screening: A clinical study,” Int. J. Comput. Sci. Eng., Vol. 21, no. 4, pp. 564–73, 2020. DOI: 10.1504/IJCSE.2020.106869
   Web of Science ®Google Scholar
• C. Szegedy, S. Ioffe, V. Vanhoucke and A. A. Alemi, “Inception-v4, Inception-ResNet and the impact of residual connections on learning,” Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 31, no. 1, pp. 4278–4284, 2017. DOI: 10.1609/aaai.v31i1.11231
   Google Scholar
• S. O. Mensah, B. Bah and W. Brink. Intelligent Computing. Lecture Notes in Networks and Systems. Vol. 285. Springer International Publishing, 2021, pp. 100–107. DOI: 10.1007/978-3-030-80129-8_8
   Google Scholar
• S. Sabour, N. Frosst and G. E. Hinton. “Dynamic routing between capsules,” arXiv preprint arXiv: 1710.09829, 2017.
   Google Scholar
• G. Kalyani, B. Janakiramaiah, A. Karuna and L. V. Narasimha Prasad, “Diabetic retinopathy detection and classification using capsule networks,” Complex Intell. Syst., pp. 1–14, 2021. DOI: 10.1007/s40747-021-00318-9
   PubMed Web of Science ®Google Scholar
• K. Bahadar Khan, A. A. Khaliq and M. Shahid, “A morphological hessian based approach for retinal blood vessels segmentation and denoising using region based otsu thresholding,” PLoS One, Vol. 11, no. 7, p. e0158996, 2016. DOI: 10.1371/journal.pone.0162581
   PubMed Web of Science ®Google Scholar
• K. B. Khan, A. A. Khaliq and M. Shahid, “A novel fast GLM approach for retinal vascular segmentation and denoising,” J. Inf. Sci. Eng, Vol. 33, no. 6, pp. 1611–27, 2017.
   Web of Science ®Google Scholar
• G. E. Hinton, A. Krizhevsky and S. D. Wang, “Transforming auto-encoders,” in Artificial Neural Networks and Machine Learning – ICANN 2011, Vol. 6791, Berlin, Heidelberg: Lecture Notes in Computer Science, Springer, 2011, pp. 44–51. DOI: 10.1007/978-3-642-21735-7_6
   Google Scholar
• G. E. Hinton, S. Sabour and N. Frosst, “Matrix capsules with em routing,” in Sixth International Conference on Learning Representations, Toronto, Canada, April 30–May 3, 2018, pp. 1–15.
   Google Scholar
• N. Muhammad Hussain, et al., “Accessing artificial intelligence for fetus health status using hybrid deep learning algorithm (AlexNet-SVM) on cardiotocographic data,” Sensors, Vol. 22, no. 14, pp. 5103, 2022. DOI: 10.3390/s22145103
   PubMed Web of Science ®Google Scholar
• E. V. Carrera, A. Gonzalez and R. Carrera, “Automated detection of diabetic retinopathy using SVM,” in IEEE XXIV International Conference on Electronics, Electrical Engineering and Computing (INTERCON), Cusco, Peru: IEEE, 2017, pp. 1–4. DOI: 10.1109/INTERCON.2017.8079692
   Google Scholar
• L. Hoang, S.-H. Lee and K.-R. Kwon, “A 3D shape recognition method using hybrid deep learning network CNN–SVM,” Electronics. (Basel), Vol. 9, no. 4, pp. 649, 2020. DOI: 10.3390/electronics9040649
   Web of Science ®Google Scholar
• M. M. Butt, et al., “Diabetic retinopathy detection from fundus images of the eye using hybrid deep learning features,” Diagnostics, Vol. 12, no. 7, pp. 1607, 2022. DOI: 10.3390/diagnostics12071607
   Web of Science ®Google Scholar
• J. P. Kandhasamy, et al., “Diagnosis of diabetic retinopathy using multi level set segmentation algorithm with feature extraction using SVM with selective features,” Multimedia Tools Appl., Vol. 79, no. 15, pp. 10581–96, 2020. DOI: 10.1007/s11042-019-7485-8
   Web of Science ®Google Scholar
• S. Maqsood, R. Damaševičius and R. Maskeliūnas, “Multi-modal brain tumor detection using deep neural network and multiclass SVM,” Medicina, Vol. 58, no. 8, p. 1090, 2022. DOI: 10.3390/medicina58081090
   PubMedGoogle Scholar
• K. M. Alabdulwahhab, W. Sami, T. Mehmood, S. A. Meo, T. A. Alasbali and F. A. Alwadani, “Automated detection of diabetic retinopathy using machine learning classifiers,” Eur. Rev. Med. Pharmacol. Sci., Vol. 25, no. 2, pp. 583–90, 2021. DOI: 10.26355/eurrev_202101_24615
   PubMed Web of Science ®Google Scholar
• R. S. Raj Kumar, T. J. Agathish Kumar, D. Ragul and A. G. Selvarani, “Transfer learning approach for diabetic retinopathy detection using residual network,” in Sixth International Conference on Inventive Computation Technologies (ICICT), Coimbatore, India: IEEE, 2021, pp. 1189–1193. DOI: 10.1109/ICICT50816.2021.9358468
   Google Scholar