Comparison of Convolutional Neural Network Model in Classification of Diabetic Retinopathy
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Abstract
Untreated diabetes mellitus will cause complications, and one of the diseases caused by it is Diabetic Retinopathy (DR). Machine learning is one of the methods that can be used to classify DR. Convolutional Neural Network (CNN) is a branch of machine learning that can classify images with reasonable accuracy. The Messidor dataset, which has 1,200 images, is often used as a dataset for the DR classification. Before training the model, we carried out several data preprocessing, such as labeling, resizing, cropping, separation of the green channel of images, contrast enhancement, and changing image extensions. In this paper, we proposed three methods of DR classification: Simple CNN, Le-Net, and DRnet model. The accuracy of testing of the several models of test data was 46.7%, 51.1%, and 58.3% Based on the research, we can see that DR classification must use a deep architecture so that the feature of the DR can be recognized. In this DR classification, DRnet achieved better accuracy with an average of 9.4% compared to Simple CNN and Le-Net model.
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References
Adarsh, P., & Jeyakumari, D. (2013). Multiclass SVM-based automated diagnosis of diabetic retinopathy. International Conference on Communication and Signal Processing, ICCSP 2013 - Proceedings, 206–210.
https://doi.org/10.1109/iccsp.2013.6577044
Al-Jawfi, R. (2009). Handwriting Arabic character recognition lenet using neural network. International Arab Journal of Information Technology, 6(3), 304–309.
Arcadu, F., Benmansour, F., Maunz, A., Willis, J., Haskova, Z., & Prunotto, M. (2019). Deep learning algorithm predicts diabetic retinopathy progression in individual patients. Npj Digital Medicine, 2(1). https://doi.org/10.1038/s41746-019-0172-3
Bora, K., Chowdhury, M., Mahanta, L. B., Kundu, M. K., & Das, A. K. (2016). Image Classification Using Convolutional Neural Networks. ACM International Conference Proceeding Series, (June). https://doi.org/10.1145/3009977.3010068
Decencière, E., Zhang, X., Cazuguel, G., Laÿ, B., Cochener, B., Trone, C., … Klein, J. C. (2014). Feedback on a publicly distributed image database: The Messidor database. Image Analysis and Stereology, 33(3), 231–234. https://doi.org/10.5566/ias.1155
Duh, E. J., Sun, J. K., & Stitt, A. W. (2017). Diabetic retinopathy: current understanding, mechanisms, and treatment strategies. JCI Insight, 2(14), 1–13. https://doi.org/10.1172/jci.insight.93751
G Alaslani, M., & A. Elrefaei, L. (2018). Convolutional Neural Network Based Feature Extraction for IRIS Recognition. International Journal of Computer Science and Information Technology, 10(2), 65–78.
https://doi.org/10.5121/ijcsit.2018.10206
Han, X., & Li, Y. L. (2015). The Application of Convolution Neural Networks in Handwritten Numeral Recognition. International Journal of Database Theory and Application, 8(3), 367–376. https://doi.org/10.14257/ijdta.2015.8.3.32
M, S., K, Laskshmi, D., Madel, M., & Kurakula, K. (2018). CONVOLUTIONAL NEURAL NETWORK FOR SUPERVISED IMAGE CLASSIFICATION. International Journal of Pure and Applied Mathematics, 119(14), 77–83.
Majaj, N. J., & Pelli, D. G. (2018). Deep learning-Using machine learning to study biological vision. Journal of Vision, 18(13), 1–13. https://doi.org/10.1167/18.13.2
Noronha, K., & Nayak, K. P. (2012). A review of fundus image analysis for the automated detection of diabetic retinopathy. Journal of Medical Imaging and Health Informatics, 2(3), 258–265. https://doi.org/10.1166/jmihi.2012.1098
Pratt, H., Coenen, F., Broadbent, D. M., Harding, S. P., & Zheng, Y. (2016). Convolutional Neural Networks for Diabetic Retinopathy. Procedia Computer Science, 90(July), 200–205. https://doi.org/10.1016/j.procs.2016.07.014
Regina Lourdhu Suganthi, S., Hanumanthappa, M., & Kavitha, S. (2018). Image classification using Deep learning. ICSNS 2018 - Proceedings of IEEE International Conference on Soft-Computing and Network Security, 7, 614–617. https://doi.org/10.1109/ICSNS.2018.8573655
Sisodia, D. S., Nair, S., & Khobragade, P. (2017). Diabetic retinal fundus images: Preprocessing and feature extraction for early detection of Diabetic Retinopathy. Biomedical and Pharmacology Journal, 10(2), 615–626. https://doi.org/10.13005/bpj/1148
Visa, S., Ramsay, B., Ralescu, A., & Van Der Knaap, E. (2011). Confusion matrix-based feature selection. CEUR Workshop Proceedings, 710, 120–127.
Vogt, M. (2019). An Overview of Machine Learning and its Applications. (January), 178–202. https://doi.org/10.1007/978-3-658-23751-6_17
Wang, W., & Lo, A. C. Y. (2018). Diabetic retinopathy: Pathophysiology and treatments. International Journal of Molecular Sciences, 19(6). https://doi.org/10.3390/ijms19061816
Xu, K., Feng, D., & Mi, H. (2017). Deep convolutional neural network-based early automated detection of diabetic retinopathy using fundus image. Molecules, 22(12). https://doi.org/10.3390/molecules22122054