Abstract
Diabetic retinopathy is an eye disease that occurs with damage to the retina and has many different complications, ranging from permanent blindness. The aim of this study is to develop a (convolutional neural network) CNN model that determines with high accuracy whether fundus images are diabetic retinopathy. The performance of the model has been verified in Kaggle APTOS 2019 dataset with AlexNET and VggNET-16 deep transfer learning algorithms. Various image processing techniques have been used as well as deep learning methods to further improve the classification performance. Images in the data set were rescaled to 224 × 224 × 3 and converted to Grayscale color space. Besides Gauss filter applied to eliminate the noise in the images. The area under the curve (AUC), precision, recall, and accuracy metrics of the deep transfer learning models used in this study were compared. The AlexNet model achieved a 98.6% AUC score, 95.2% accuracy, and the VggNET-16 model achieved a 99.6% AUC score and 98.1% accuracy. VggNET-16 was found to have higher confidence. Our results show that with the correct optimization of the CNN model applied in diabetic retinopathy classification, deep transfer learning models can achieve high performance and can be used in the detection of diabetic retinopathy patients.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Sahlsten, J., et al.: Deep learning fundus image analysis for diabetic retinopathy and macular edema grading. Sci. Rep. 9(1), 1–11 (2019)
De La Torre, J., Valls, A., Puig, D.: A deep learning interpretable classifier for diabetic retinopathy disease grading. Neurocomputing 396, 465–476 (2020)
Zago, G.T., Andreão, R.V., Dorizzi, B., Salles, E.O.T.: Diabetic retinopathy detection using red lesion localization and convolutional neural networks. Comput. Biol. Med. 116, 103537 (2020)
Pratt, H., Coenen, F., Broadbent, D.M., Harding, S.P., Zheng, Y.: Convolutional neural networks for diabetic retinopathy. Procedia Comput. Sci. 90, 200–205 (2016)
Sarki, R., Michalska, S., Ahmed, K., Wang, H., Zhang, Y.: Convolutional neural networks for mild diabetic retinopathy detection: an experimental study. bioRxiv 763136 (2019)
Jain, A., Jalui, A., Jasani, J., Lahoti, Y., Karani, R.: Deep learning for detection and severity classification of diabetic retinopathy. In: 2019 1st International Conference on Innovations in Information and Communication Technology, pp. 1–6. IEEE (2019)
Shaban, M., et al.: Automated staging of diabetic retinopathy using a 2d convolutional neural network. In: 2018 International Symposium on Signal Processing and Information Technology, pp. 354–358. IEEE (2018)
Lam, C., Yi, D., Guo, M., Lindsey, T.: Automated detection of diabetic retinopathy using deep learning. In: AMIA Summits on Translational Science Proceedings (2018)
Hagos, M.T., Kant, S.: Transfer learning based detection of diabetic retinopathy from small dataset. arXivpreprint arXiv 1905 07203 (2019)
APTOS 2019 Blindness Detection. https://www.kaggle.com/c/aptos2019-blindness-detection/data. Accessed 08 Feb 2021
Islam, M.R., Hasan, M.A.M., Sayeed, A.: Transfer learning based diabetic retinopathy detection with a novel preprocessed layer. In: 2020 IEEE Region 10 Symposium TENSYMP, Dhaka, Bangladesh, pp. 888–891 (2020)
Taufiqurrahman, S., Handayani, A., Hermanto, B.R., Mengko, T.L.E.R.: Diabetic retinopathy classification using a hybrid and efficient MobileNetV2-SVM model. In: 2020 IEEE REGION 10 CONFERENCE (TENCON), Osaka, Japan, pp. 235–240 (2020)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Adv. Neural. Inf. Process. Syst. 25, 1097–1105 (2012)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXivpreprint arXiv 1409 1556 (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Çinarer, G., Kiliç, K. (2022). Diabetic Retinopathy Detection with Deep Transfer Learning Methods. In: Kahraman, C., Cebi, S., Cevik Onar, S., Oztaysi, B., Tolga, A.C., Sari, I.U. (eds) Intelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation. INFUS 2021. Lecture Notes in Networks and Systems, vol 308. Springer, Cham. https://doi.org/10.1007/978-3-030-85577-2_17
Download citation
DOI: https://doi.org/10.1007/978-3-030-85577-2_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-85576-5
Online ISBN: 978-3-030-85577-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)