ABSTRACT
Diabetic Retinopathy is considered as one of the significant reasons for vision impairment. Its identification involves detecting the presence of some features in retinal fundus images by clinicians which is a time and resource consuming procedure and a difficult manual diagnosis. In this article, a deep learning-based approach using Deep Convolutional Neural Network is developed for the diagnosis of Diabetic Retinopathy. By classifying from retinal fundus images with its severity level, it is possible to detect Diabetic Retinopathy. A Diabetic Retinopathy classifier is constructed followed by a transfer learning technique, DenseNet architecture based pre-trained model. Identification of Diabetic Retinopathy is done by detecting the presence of features like micro-aneurysms, exudates, hemorrhages in retinal images. We have also shown the preprocessing and augmentation of image data that benefits the model to detect retinopathy. After the training and validating procedure, the developed classifier achieves significant training accuracy of 96.3% and validation accuracy of 94.9% along with 0.88 quadratic weighted kappa.
- Ronald Klein, Barbara E.K. Klein, and Scot E. Moss. 1984. Visual Impairment in Diabetes. Ophthalmology 91, 1 (Jan. 1984), 1--9. https://doi.org/10.1016/S0161-6420(84)34337-8Google ScholarCross Ref
- Congdon, N.-G., Friedman, D.-S., Lietman, T. Important causes of visual impairment in the world today. Jama 290, 15 (2003), 2057-2060. http://doi.org/10.1001/jama.290.15.2057Google ScholarCross Ref
- M. U. Akram, S. Khalid, and S. A. Khan. 2012. Identification and classification of microaneurysms for early detection of diabetic retinopathy. Pattern Recognition 46, (July 2012), 107--112. http://doi.org/10.1016/j.patcog.2012.07.002Google ScholarDigital Library
- Lorina Leung, OD. 2018. How does diabetic retinopathy cause vision loss? (May, 2018). Retrieved July 22, 2019 from https://drlleung.com/blog/f/how-does-diabetic-retinopathy-cause-vision-lossGoogle Scholar
- Acharya, U. R., Lim, C. M., Ng, E. Y. K., Chee, C., and Tamura, T. 2009. Computer-based detection of diabetes retinopathy stages using digital fundus images. In Proceedings of the Institution of Mechanical Engineers. Journal of Engineering in Medicine, 545--553. https://doi.org/10.1243/09544119JEIM486Google ScholarCross Ref
- N. Silberman, K. Ahrlich, R. Fergus, and L. Subramanian. 2010. Case for Automated Detection of Diabetic Retinopathy. In AAAI Spring Symposium. 85--90. https://www.aaai.org/ocs/index.php/SSS/SSS10/paper/viewPaper/1186Google Scholar
- G. Gardner, D. Keating, T. Williamson, and A. Elliott. 1996. Automatic detection of diabetic retinopathy using an artificial neural network: a screening tool. British Journal of Ophthalmology 80, 11 (Nov. 1996), 940--944. http://doi.org/10.1136/bjo.80.11.940Google ScholarCross Ref
- J. Nayak, P. S. Bhat, U. R. Acharya, and M. Kagathi. 2007. Automated Identification of Diabetic Retinopathy Stages Using Digital Fundus Images. Journal of Medical Systems 32, 2 (April 2008), 107--115. https://doi.org/10.1007/s10916-007-9113-9Google ScholarDigital Library
- Z. Wang, and J. Yang. Diabetic Retinopathy Detection via Deep Convolutional Networks for Discriminative Localization and Visual Explanation. arXiv:1703.10757. Retrieved from https://arxiv.org/abs/1703.10757Google Scholar
- H. Pratt, F. Coenen, D. M Broadbent, S. P. Harding, and Y. Zhen. 2016. Convolutional Neural Network for Diabetic Retinopathy. In 20th Conference on Medical Image Understanding and Analysis (MIUA 2016). Procedia Computer Science, 200--205. https://doi.org/10.1016/j.procs.2016.07.014Google ScholarCross Ref
- M. A. Bravo and P. A. Arbeláez. 2017. Automatic diabetic retinopathy classification. In 13th International Conference on Medical Information Processing and Analysis. Proc. SPIE 10572. https://doi.org/10.1117/12.2285939Google ScholarCross Ref
- S. Wan, Y. Liang, and Y. Zhang. 2018. Deep convolutional neural networks for diabetic retinopathy detection by image classification. Computers and Electrical Engineering 72, (Oct. 2018), 274--282. https://doi.org/10.1016/j.compeleceng.2018.07.042Google ScholarCross Ref
- Kaggle. 2019. Kaggle: Your Home for Data Science. Retrieved from https://www.kaggle.com/.Google Scholar
- G. Huang, Z. Liu, L. van der Maaten, and K. Q. Weinberger. Densely Connected Convolutional Networks. arXiv:1608.06993. Retrieved from https://arxiv.org/abs/1608.06993Google Scholar
- J. Deng, W. Dong, R. Socher, L. Li, Kai Li, and Li Fei-Fei. 2009. ImageNet: A large-scale hierarchical image database. In 2009 IEEE Conference on Computer Vision and Pattern Recognition. Miami, FL, 248--255. https://doi.org/10.1109/CVPR.2009.5206848Google ScholarCross Ref
- Kauppi, T., Kalesnykiene, V., Kamarainen, J.-K., Lensu, L., Sorri, I., Uusitalo, H., Kälviäinen, H., and Pietilä, J. 2006. DIARETDB0: Evaluation Database and Methodology for Diabetic Retinopathy Algorithms. Machine Vision and Pattern Recognition Research Group. Laboratory of Information Processing. Lappeenranta University of Technology. Lappeenranta, Finland.Google Scholar
- Kauppi, T., Kalesnykiene, V., Kamarainen, J.-K., Lensu, L., Sorri, I., Raninen A., Voutilainen R., Uusitalo, H., Kälviäinen, H., Pietilä, J. 2007. DIARETDB1 diabetic retinopathy database and evaluation protocol. Machine Vision and Pattern Recognition Research Group. Laboratory of Information Processing. Lappeenranta University of Technology. Lappeenranta, Finland.Google Scholar
- Kaggle. Diabetic Retinopathy Detection. Identify signs of diabetic retinopathy in eye images. Retrieved from https://www.kaggle.com/c/diabetic-retinopathy-detectionGoogle Scholar
- Kaggle. APTOS 2019 Blindness Detection. Detect diabetic retinopathy to stop blindness before it's too late. Retrieved from https://www.kaggle.com/c/aptos2019-blindness-detectionGoogle Scholar
- Ben Graham. 2015. Kaggle diabetic retinopathy detection competition report.Google Scholar
- S. M. Vieira, U. Kaymak and J. M. C. Sousa. 2010. Cohen's kappa coefficient as a performance measure for feature selection. In International Conference on Fuzzy Systems. Barcelona, Spain, 1-8. https://doi.org/10.1109/FUZZY.2010.5584447Google ScholarCross Ref
- Arie Ben-David. About the relationship between ROC curves and Cohen's kappa. Engineering Applications of Artificial Intelligence 21, 6 (Sept. 2008), 874--882. https://doi.org/10.1016/j.engappai.2007.09.009Google ScholarDigital Library
Index Terms
- An Automated Model using Deep Convolutional Neural Network for Retinal Image Classification to Detect Diabetic Retinopathy
Recommendations
Retinal Vessel Segmentation of Non-Proliferative Diabetic Retinopathy
Diabetic retinopathy is a disease in diabetic patients that affects the eye. It happens due to damage in the blood vessels of the light-sensitive tissues at the retina. In non-proliferative diabetic retinopathy, tiny changes occur in the blood vessels ...
Analysis of Pre-trained Convolutional Neural Network Models in Diabetic Retinopathy Detection Through Retinal Fundus Images
Computer Information Systems and Industrial ManagementAbstractDiabetic Retinopathy (DR) is a disease on the rise; as this is a complication of diabetes, it becomes an imminent fate in people who have not been treated correctly for the disease, resulting in possible loss of vision if not is detected in time. ...
An automated retinal imaging method for the early diagnosis of diabetic retinopathy
BACKGROUND: Diabetic retinopathy is a microvascular complication of long-term diabetes and is the major cause for eyesight loss due to changes in blood vessels of the retina. Major vision loss due to diabetic retinopathy is highly preventable with ...
Comments