Convolutional Neural Network-Based Classification Models for the Detection of Diabetic Retinopathy in Retinal Fundus
Abstract
Diabetes mellitus is a widespread concern worldwide and the most serious microvascular diseases that usually occur as a result are those of the eye, which include the retinopathy and macular edema. Over the past decade, DR has emerged as a significant player in causing vision disability and blindness. Provided that diabetes-related eye complications are promptly diagnosed and handled, the consequences of them may be significantly improved and the level of sugar in the blood can be kept on an adequate level. Nevertheless, the DR symptoms are not consistent and may be complicated; thus, doctors may spend a lot of time to diagnose them.One of the approaches to detecting and classifying DR on fundus retina photographs that is taken into account in the paper is the one which relies on CNNs and deep learning. All the experimental data used in the present study was taken at the Department of Ophthalmology at Xiangya No. 2 Hospital at Changsha in China. The sample of cases is not considerable and the information included in this dataset is imbalanced. That is why a system was made that can be used to rectify the variety and excellence of the information utilized in the training by normalizing and creating information.Then, many CNNs such as "ResNet"-101, "ResNet"-50, and "VGGNet"-16 were employed to ascertain the phases of DR. "ResNet"-101 outperformed the other models by getting 98.88% accuracy and losing 0.3499 during training and 0.9882 during testing. The model was checked on datasets such as HRF, STARE, DIARETDB0, and XHO, which contain 1,787 examples and resulted in an average accuracy of 97%, making it higher than existing methods on the same subject. As a result, using this proposed model enhances DR detection accuracy more than "ResNet"-50 and "VGGNet"-16, making it promising for DR screening in health services.
References
R. Reguant, S. Brunak, and S. Saha, “Understanding inherent image features in CNN-based assessment of diabetic retinopathy,” Sci. Rep., vol. 11, p. 9704, 2021.
F. Aguirre, A. Brown, N. H. Cho, G. Dahlquist, S. Dodd, T. Dunning, M. Hirst, C. Hwang, D. Magliano, C. Patterson, et al., IDF Diabetes Atlas, 6th ed. Basel, Switzerland: Int. Diabetes Fed., 2013.
D. S. Ting, K. A. Tan, V. Phua, G. S. Tan, C. W. Wong, and T. Y. Wong, “Biomarkers of diabetic retinopathy,” Curr. Diabetes Rep., vol. 16, p. 125, 2016.
R. R. Bourne, G. A. Stevens, R. A. White, J. L. Smith, S. R. Flaxman, H. Price, J. B. Jonas, J. Keeffe, J. Leasher, K. Naidoo, et al., “Causes of vision loss worldwide, 1990–2010: A systematic analysis,” Lancet Glob. Health, vol. 1, no. 6, pp. e339–e349, 2013.
N. Cheung, P. Mitchell, and T. Y. Wong, “Diabetic retinopathy,” Lancet, vol. 376, pp. 124–136, 2010.
K. Kroenke, “Telemedicine screening for eye disease,” JAMA, vol. 313, pp. 1666–1667, 2015.
DCCT/EDIC Research Group, D. M. Nathan, I. Bebu, D. Hainsworth, R. Klein, W. Tamborlane, G. Lorenzi, R. Gubitosi-Klug, and J. M. Lachin, “Frequency of evidence-based screening for retinopathy in type 1 diabetes,” N. Engl. J. Med., vol. 376, pp. 1507–1516, 2017.
J. C. Chan, V. Malik, W. Jia, T. Kadowaki, C. S. Yajnik, K. H. Yoon, and F. B. Hu, “Diabetes in Asia: Epidemiology, risk factors, and pathophysiology,” JAMA, vol. 301, pp. 2129–2140, 2009.
N. G. Congdon, D. S. Friedman, and T. Lietman, “Important causes of visual impairment in the world today,” JAMA, vol. 290, pp. 2057–2060, 2003.
S. M. Marshall and A. Flyvbjerg, “Prevention and early detection of vascular complications of diabetes,” BMJ, vol. 333, pp. 475–480, 2006.
A. Hutchinson, A. McIntosh, J. Peters, C. O’keeffe, K. Khunti, R. Baker, and A. Booth, “Effectiveness of screening and monitoring tests for diabetic retinopathy—A systematic review,” Diabet. Med., vol. 17, pp. 495–506, 2000.
R. Taylor and D. Batey, Handbook of Retinal Screening in Diabetes: Diagnosis and Management. Hoboken, NJ, USA: Wiley, 2012.
V. Gulshan, L. Peng, M. Coram, M. C. Stumpe, D. Wu, A. Narayanaswamy, S. Venugopalan, K. Widner, T. Madams, J. Cuadros, et al., “Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs,” JAMA, vol. 316, pp. 2402–2410, 2016.
D. S. W. Ting, C. Y. L. Cheung, G. Lim, G. S. W. Tan, N. D. Quang, A. Gan, H. Hamzah, R. Garcia-Franco, I. Y. San Yeo, S. Y. Lee, et al., “Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes,” JAMA, vol. 318, pp. 2211–2223, 2017.
S. Chaudhuri, S. Chatterjee, N. Katz, M. Nelson, and M. Goldbaum, “Detection of blood vessels in retinal images using two-dimensional matched filters,” IEEE Trans. Med. Imaging, vol. 8, no. 3, pp. 263–269, 1989.
C. Sinthanayothin, J. F. Boyce, H. L. Cook, and T. H. Williamson, “Automated localisation of the optic disc, fovea, and retinal blood vessels from digital colour fundus images,” Br. J. Ophthalmol., vol. 83, pp. 902–910, 1999.
C. E. Baudoin, B. J. Lay, and J. C. Klein, “Automatic detection of microaneurysms in diabetic fluorescein angiography,” Rev. Epidemiol. Sante Publique, vol. 32, pp. 254–261, 1984.
N. Silberman, K. Ahrlich, R. Fergus, and L. Subramanian, “Case for automated detection of diabetic retinopathy,” in Proc. AAAI Spring Symp. Series, Stanford, CA, USA, Mar. 22–24, 2010. [Online]. Available: https://nyuscholars.nyu.edu/en/publications/case-for-automated-detection-of-diabetic-retinopathy
D. G. Lowe, “Distinctive image features from scale-invariant key points,” Int. J. Comput. Vis., vol. 60, pp. 91–110, 2004.
T. Lindeberg, “Scale-invariant feature transform,” Scholarpedia, vol. 7, p. 10491, 2012.
R. Pires, H. F. Jelinek, J. Wainer, E. Valle, and A. Rocha, "Advancing bag-of-visual-words representations for lesion classification in retinal images," PLoS ONE, vol. 9, p. e96814, 2014, doi: 10.1371/journal.pone.0096814.
H. Bay, T. Tuytelaars, and L. Van Gool, "Surf: Speeded up robust features," in European Conference on Computer Vision, Berlin, Heidelberg: Springer, 2006, pp. 404–417.
L. Séoud, T. Faucon, T. Hurtut, J. Chelbi, F. Cheriet, and J. P. Langlois, "Automatic detection of microaneurysms and haemorrhages in fundus images using dynamic shape features," in Proc. IEEE 11th Int. Symp. Biomed. Imaging (ISBI), Beijing, China, Apr. 2014, pp. 101–104.
L. Seoud, J. Chelbi, and F. Cheriet, "Automatic grading of diabetic retinopathy on a public database," in Ophthalmic Medical Image Analysis Int. Workshop, Iowa City, IA, USA: Univ. of Iowa, 2015.
M. J. Paranjpe and M. N. Kakatkar, "Automated diabetic retinopathy severity classification using support vector machine," Int. J. Res. Sci. Adv. Technol., vol. 3, pp. 86–91, 2013.
A. Liaw and M. Wiener, "Classification and regression by randomForest," R News, vol. 2, pp. 18–22, 2002.
T. Joachims, Making large-scale SVM learning practical, Tech. Rep., Univ. of Dortmund, 1998. [Online]. Available: https://www.econstor.eu/handle/10419/77178
T. Walter, J. C. Klein, P. Massin, and A. Erginay, "A contribution of image processing to the diagnosis of diabetic retinopathy—detection of exudates in color fundus images of the human retina," IEEE Trans. Med. Imaging, vol. 21, pp. 1236–1243, 2002.
M. D. Zeiler and R. Fergus, "Visualizing and understanding convolutional networks," in European Conf. Computer Vision (ECCV), Zurich, Switzerland, Sep. 2014, Cham: Springer, pp. 818–833.
Z. Wang and J. Yang, "Diabetic retinopathy detection via deep convolutional networks for discriminative localization and visual explanation," in Proc. Workshops 32nd AAAI Conf. Artif. Intell., New Orleans, LA, USA, Feb. 2017.
Y. Yang, T. Li, W. Li, H. Wu, W. Fan, and W. Zhang, "Lesion detection and grading of diabetic retinopathy via two-stages deep convolutional neural networks," in Int. Conf. Med. Image Computing and Computer-Assisted Intervention (MICCAI), Cham: Springer, 2017, pp. 533–540.
S. Wang, Y. Yin, G. Cao, B. Wei, Y. Zheng, and G. Yang, "Hierarchical retinal blood vessel segmentation based on feature and ensemble learning," Neurocomputing, vol. 149, pp. 708–717, 2015.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, "Imagenet classification with deep convolutional neural networks," Adv. Neural Inf. Process. Syst., vol. 25, pp. 1097–1105, 2012.
C. Szegedy, S. Ioffe, V. Vanhoucke, and A. A. Alemi, "Inception-v4, inception-ResNet and the impact of residual connections on learning," in Proc. 31st AAAI Conf. Artif. Intell., San Francisco, CA, USA, Feb. 2017.
O. M. Parkhi, A. Vedaldi, and A. Zisserman, Deep Face Recognition, Univ. of Oxford, 2015. [Online]. Available: https://www.robots.ox.ac.uk/~vgg/publications/2015/Parkhi15/parkhi15.pdf
K. Simonyan and A. Zisserman, "Very deep convolutional networks for large-scale image recognition," arXiv preprint arXiv:1409.1556, 2014.
C. Szegedy et al., "Going deeper with convolutions," in Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA, Jun. 2015, pp. 1–9.
K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," in Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, Jun. 2016, pp. 770–778.
S. Wan, Y. Liang, and Y. Zhang, "Deep convolutional neural networks for diabetic retinopathy detection by image classification," Comput. Electr. Eng., vol. 72, pp. 274–282, 2018.
W. L. Alyoubi, M. F. Abulkhair, and W. M. Shalash, "Diabetic retinopathy fundus image classification and lesions localization system using deep learning," Sensors, vol. 21, p. 3704, 2021.
M. K. Behera, R. Mishra, A. Ransingh, and S. Chakravarty, “Prediction of different stages in diabetic retinopathy from retinal fundus images using radial basis function based SVM,” Indian J. Sci. Technol., vol. 13, pp. 2030–2040, 2020. [CrossRef]
M. Sankar, K. Batri, and R. Parvathi, “Earliest diabetic retinopathy classification using deep convolution neural networks,” Int. J. Adv. Eng. Technol., vol. 10, p. M9, 2016.
G. Lim, M. L. Lee, W. Hsu, and T. Y. Wong, “Transformed representations for convolutional neural networks in diabetic retinopathy screening,” in Proc. Workshops at the 28th AAAI Conf. on Artificial Intelligence, Québec City, QC, Canada, Jul. 2014.
H. Pratt, F. Coenen, D. M. Broadbent, S. P. Harding, and Y. Zheng, “Convolutional neural networks for diabetic retinopathy,” Procedia Comput. Sci., vol. 90, pp. 200–205, 2016. [CrossRef]
Kaggle, “Diabetic retinopathy detection challenge,” 2016. [Online]. Available: http://www.kaggle.com/c/diabetic-retinopathy-detection/data. [Accessed: Jun. 1, 2022].
B. Zhou, A. Khosla, A. Lapedriza, A. Oliva, and A. Torralba, “Learning deep features for discriminative localization,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, Jun. 2016, pp. 2921–2929.
M. Lin, Q. Chen, and S. Yan, “Network in network,” arXiv preprint arXiv:1312.4400, 2013.
S. A. Lekshmi and G. M. Rajathi, “Detection of glaucoma and diabetic retinopathy using image processing technique by Raspberry Pi,” Indian J. Sci. Technol., vol. 12, p. 29, 2019. [CrossRef]
C. H. Bindu and G. S. Sravanthi, “Retinopathy detection of eye images in diabetic patients,” Indian J. Sci. Technol., vol. 12, p. 3, 2019. [CrossRef]
R. Sarki, K. Ahmed, H. Wang, Y. Zhang, J. Ma, and K. Wang, “Image preprocessing in classification and identification of diabetic eye diseases,” Data Sci. Eng., vol. 6, pp. 455–471, 2021. [CrossRef]
A. G. P. Henry and A. Jude, “Convolutional neural-network-based classification of retinal images with different combinations of filtering techniques,” Open Comput. Sci., vol. 11, pp. 480–490, 2021. [CrossRef]
M. Frid-Adar, E. Klang, M. Amitai, J. Goldberger, and H. Greenspan, “Synthetic data augmentation using GAN for improved liver lesion classification,” in Proc. 2018 IEEE 15th Int. Symp. Biomedical Imaging (ISBI), Washington, DC, USA, Apr. 2018, pp. 289–293.
University of Erlangen-Nuremberg, High Resolution Fundus (HRF) Image Database. Erlangen, Germany: Univ. Erlangen-Nuremberg, 2014. [Online]. Available: http://www5.cs.fau.de/research/data/fundus-images/. [Accessed: May 2, 2021].
M. Goldbaum, Structured Analysis of the Retina, 2013. [Online]. Available: http://www.ces.clemson.edu/ahoover/stare/. [Accessed: May 24, 2021].
DIARETDB0 Database, Medical Image Understanding and Analysis, Mar. 2014. [Online]. Available: http://www.it.lut.fi/project/imageret/diaretdb. [Accessed: Mar. 24, 2022].
E. Decenciere, X. Zhang, G. Cazuguel, B. Lay, B. Cochener, and C. Trone, “Feedback on a publicly distributed image database: The MESSIDOR database,” Image Anal. Stereol., vol. 33, pp. 231–234, 2014. [CrossRef]
T. Li, Y. Gao, K. Wang, S. Guo, H. Liu, and H. Kang, “Diagnostic assessment of deep learning algorithms for diabetic retinopathy screening,” Inf. Sci., vol. 501, pp. 511–522, 2019. [CrossRef]
Y. P. Liu, Z. Li, C. Xu, J. Li, and R. Liang, “Referable diabetic retinopathy identification from eye fundus images with weighted path for convolutional neural network,” Artif. Intell. Med., vol. 99, p. 101694, 2019. [CrossRef]
H. Jiang, K. Yang, M. Gao, D. Zhang, H. Ma, and W. Qian, “An interpretable ensemble deep learning model for diabetic retinopathy disease classification,” in Proc. 2019 41st Annu. Int. Conf. IEEE Eng. Medicine and Biology Society (EMBC), Berlin, Germany, Jul. 2019, pp. 2045–2048.
S. Das, K. Kharbanda, M. Suchetha, R. Raman, and E. Dhas, “Deep learning architecture based on segmented fundus image features for classification of diabetic retinopathy,” Biomed. Signal Process. Control., vol. 68, p. 102600, 2021. [CrossRef]
X. Wang, Y. Lu, Y. Wang, and W. B. Chen, “Diabetic retinopathy stage classification using convolutional neural networks,” in Proc. 2018 IEEE Int. Conf. Information Reuse and Integration (IRI), Salt Lake City, UT, USA, Jul. 2018, pp. 465–471.
S. H. Khan, Z. Abbas, and S. D. Rizvi, “Classification of diabetic retinopathy images based on customised CNN architecture,” in Proc. 2019 Amity Int. Conf. Artificial Intelligence (AICAI), Dubai, UAE, Feb. 2019, pp. 244–248.
T. Shanthi and R. S. Sabeenian, “Modified AlexNet architecture for classification of diabetic retinopathy images,” Comput. Electr. Eng., vol. 76, pp. 56–64, 2019. [CrossRef]
