Abstract
People with diabetes risk developing an eye disease called diabetic Retinopathy. It happens when high blood glucose levels cause damage to blood vessels within the retina. These blood vessels may swell, leak or close, stopping blood from passing through. Sometimes new blood vessels may grow on the retina. All of these results can steal the eye vision. Generally, for the diagnosis and detection of this disease, skilled professionals must detect this disease using images of the patient's retina. But due to recent development and improvement in deep learning, this task can be done very efficiently and easily using advanced techniques in deep understanding. We have implemented multiple states of the art DNN architecture like InceptionV3, VGG net, and ResNet with transfer learning. We have used Gaussian blur with some filters as preprocessing the image, and it is found that it gives better results. This also helped to remove unwanted noise from the image. In this work, the dataset contained images of five different D.R. classes (No D.R., Mild, Moderate, Proliferate DR, Severe) is used. After training multiple models, InceptionV3 had the best result with an accuracy of 81.2% on training data and 79.4% on testing data, so we chose it.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Pandey, S., Sharma, V.: World diabetes day 2018: Battling the emerging epidemic of diabetic retinopathy. Ind. J. Ophthalmol. 66(11), 1652 (2018)
topconhealth.com: Diabetic retinopathy: an eye disease with 4 stages. https://www.topconhealth.com/diabetic-retinopathy-an-eye-disease-with-4-stages/
Dutta, S., Manideep, B.C., Basha, S.M., Caytiles, R.D., Iyengar, N.C.: Classification of diabetic retinopathy images by using deep learning models. Int. J. Grid Distrib. Comput. 11(1), 99–106 (2018). https://doi.org/10.14257/ijgdc.2018.11.1.09
Arcadu, F., Benmansour, F., Maunz, A., Willis, J., Haskova, Z., Prunotto, M.: Deep learning algorithm predicts diabetic retinopathy progression in individual patients. Npj Digit. Med. 2(1) (2019). https://doi.org/10.1038/s41746-019-0172-3
Chakrabarty, N.: A deep learning method for the detection of diabetic retinopathy. In: 2018 5th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON) (2018). https://doi.org/10.1109/upcon.2018.8596839
Rajalakshmi, R., Subashini, R., Anjana, R.M., Mohan, V.: Automated diabetic retinopathy detection in smartphone-based fundus photography using artificial intelligence. Eye 32(6), 1138–1144 (2018). https://doi.org/10.1038/s41433-018-0064-9
Verma, K., Deep, P., Ramakrishnan, A.G.: Detection and classification of diabetic retinopathy using retinal images. In: 2011 Annual IEEE India Conference (2011). https://doi.org/10.1109/indcon.2011.6139346
Li, F., Liu, Z., Chen, H., Jiang, M., Zhang, X., Wu, Z.: Automatic detection of diabetic retinopathy in retinal fundus photographs based on deep learning algorithm. Transl. Vis. Sci. Technol. 8(6), 4 (2019). https://doi.org/10.1167/tvst.8.6.4
Athira, T.R., Sivadas, A., George, A., Paul, A., Gopan, N.R.: Automatic detection of diabetic retinopathy using R-CNN. Int. Res. J. Eng. Technol. (IRJET) 5595–5600
Jiang, H., Yang, K., Gao, M., Zhang, D., Ma, H., Qian, W.: An interpretable ensemble deep learning model for diabetic retinopathy disease classification. In: International Conference of IEEE Engineering in Medicine and Biology Society (EMBC) (2019)
Bajaj, R., Kulkarni, N., Garg, S.: Diabetic retinopathy stage classification. SSRN Electron. J. (2020). https://doi.org/10.2139/ssrn.3645460
Diabetic retinopathy resized from: https://www.kaggle.com/sohaibanwaar1203/diabetic-retinopathy-full
Wang, X., Lu, Y., Wang, Y., Chen, W.: Diabetic retinopathy stage classification using convolutional neural networks. In: 2018 IEEE International Conference on Information Reuse and Integration (IRI) (2018). https://doi.org/10.1109/iri.2018.00074
Milton-Barker.: Inception-v3-deep-convolutional-architecture-for-classifying-acute-myeloidlymphoblastic. (2019)
Deshpande, P.S., Sharma, S.C., Peddoju, S.K.: Predictive and prescriptive analytics in big-data era. In: Security and Data Storage Aspect in Cloud Computing. Studies in Big Data, vol. 52, pp. 71–81. Springer, Singapore (2019). https://doi.org/10.1007/978-981-13-6089-3_5
Alban M., Gilligan T.: Automated detection of diabetic retinopathy using fluorescein angiography photographs. Stanford Tech. Rep. (2016)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Khan, A., Kulkarni, N., Kumar, A., Kamat, A. (2022). D-CNN and Image Processing Based Approach for Diabetic Retinopathy Classification. In: Iyer, B., Ghosh, D., Balas, V.E. (eds) Applied Information Processing Systems . Advances in Intelligent Systems and Computing, vol 1354. Springer, Singapore. https://doi.org/10.1007/978-981-16-2008-9_27
Download citation
DOI: https://doi.org/10.1007/978-981-16-2008-9_27
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-2007-2
Online ISBN: 978-981-16-2008-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)