MCBM: IMPLEMENTATION OF MULTICLASS AND TRANSFER LEARNING ALGORITHM BASED ON DEEP LEARNING MODEL FOR EARLY DETECTION OF DIABETIC RETINOPATHY
DOI:
https://doi.org/10.11113/aej.v13.19401Keywords:
Diabetic Retinopathy, Deep Learning, Convolution neural network, Efficient-Net, Feature Extraction.Abstract
Diabetic retinopathy (DR), the primary cause of a visible disease in working-age adults, is often controlled with the aid of early detection to prevent sight loss. This research proposes a collection of automated deep-learning techniques for DR screening. In this paper, we collected total 3662 Images from the Kaggle. Out of the total 3662 images, 90% (3295) images taken for the training purpose and 10% (367) for the testing purpose. This study measured the performance and comparative study of five Deep Learning models such as CNN, Efficient Net, VGG 16, Mobile Net, and RESNET 50 are demonstrated to improve the accuracy by changing various parameters of these models to classify DR in different stages. Out of the total images. Our finding shows that Efficient Net achieved a training accuracy of 0.9342 and a testing accuracy is 0.8181 and RESNET 50 achieved 0.9329 accuracies for the train data set and the test data set with 0.8116 accuracies. Efficient Net and Res Net 50 have achieved better accuracy out of the five models. Hence these two models perform well as compared to the other 3 Models.
References
Yang, Y., Li, T., Li, W., Wu, H., Fan, W. and Zhang, W., 2017. Lesion detection and grading of diabetic retinopathy via two-stages deep convolutional neural networks. In Medical Image Computing and Computer Assisted Intervention− MICCAI 2017: 20th International Conference, Quebec City, QC, Canada, September 11-13, 2017, Proceedings, Part III 20: 533-540. Springer International Publishing.
Luo, Y., Pan, J., Fan, S., Du, Z. and Zhang, G., 2020. Retinal image classification by self-supervised fuzzy clustering network. IEEE Access, 8: 92352-92362.
Voets, M., Møllersen, K. and Bongo, L.A., 2019. Reproduction study using public data of: Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. PloS one, 14(6): e0217541.
Tan, J.H., Fujita, H., Sivaprasad, S., Bhandary, S.V., Rao, A.K., Chua, K.C. and Acharya, U.R., 2017. Automated segmentation of exudates, haemorrhages, microaneurysms using single convolutional neural network. Information sciences, 420: 66-76.
Sambyal, N., Saini, P., Syal, R. and Gupta, V., 2020. Modified U-Net architecture for semantic segmentation of diabetic retinopathy images. Biocybernetics and Biomedical Engineering, 40(3): 1094-1109.
Grzybowski, A., Brona, P., Lim, G., Ruamviboonsuk, P., Tan, G.S., Abramoff, M. and Ting, D.S., 2020. Artificial intelligence for diabetic retinopathy screening: a review. Eye, 34(3): 451-460.
Badar, M., Haris, M. and Fatima, A., 2020. Application of deep learning for retinal image analysis: A review. Computer Science Review, 35: 100203.
Lee, C.S., Baughman, D.M. and Lee, A.Y., 2017. Deep learning is effective for classifying normal versus age-related macular degeneration OCT images. Ophthalmology Retina, 1(4): 322-327.
Shen, Y., Sheng, B., Fang, R., Li, H., Dai, L., Stolte, S., Qin, J., Jia, W. and Shen, D., 2020. Domain-invariant interpretable fundus image quality assessment. Medical image analysis, 61: 101654.
Zago, G.T., Andreão, R.V., Dorizzi, B. and Salles, E.O.T., 2020. Diabetic retinopathy detection using red lesion localization and convolutional neural networks. Computers in biology and medicine, 116: 103537.
Sadda, S.R., Nittala, M.G., Taweebanjongsin, W., Verma, A., Velaga, S.B., Alagorie, A.R., Sears, C.M., Silva, P.S. and Aiello, L.P., 2020. Quantitative assessment of the severity of diabetic retinopathy. American Journal of Ophthalmology, 218: 342-352.
Meshram, A., Dembla, D. and Ajmera, R., 2021. Analysis and Design of Deep Learning Algorithms for Retinal Image Classification for Early Detection of Diabetic Retinopathy. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12(6): 2633-2641.
Harangi, B., Toth, J. and Hajdu, A., 2018, July. Fusion of deep convolutional neural networks for microaneurysm detection in color fundus images. In 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) 3705-3708. IEEE.
Playout, C., Duval, R. and Cheriet, F., 2018. A multitask learning architecture for simultaneous segmentation of bright and red lesions in fundus images. In Medical Image Computing and Computer Assisted Intervention–MICCAI 2018: 21st International Conference, Granada, Spain, September 16-20, 2018, Proceedings, Part II 11: 101-108. Springer International Publishing.
Benzamin, A. and Chakraborty, C., 2018, June. Detection of hard exudates in retinal fundus images using deep learning. In 2018 Joint 7th International Conference on Informatics, Electronics & Vision (ICIEV) and 2018 2nd International Conference on Imaging, Vision & Pattern Recognition (icIVPR) 465-469. IEEE.
Mushtaq, G. and Siddiqui, F., 2021, February. Detection of diabetic retinopathy using deep learning methodology. In IOP conference series: materials science and engineering. 1070(1): 012049). IOP Publishing.
Li, F., Yuan, D., Zhang, M., Liang, C., Zhou, X. and Zhang, H., 2019, July. Multi-scale stepwise training strategy of convolutional neural networks for diabetic retinopathy severity assessment. In 2019 International Joint Conference on Neural Networks (IJCNN) 1-5. IEEE.
Preetha, S., Chandan, N., Darshan, N.K. and Gowrav, P.B., 2020. Diabetes disease prediction using machine learning. Int. J. Mod. Trends Eng. Res, 6: 37-43.
Mookiah, M.R.K., Acharya, U.R., Chua, C.K., Lim, C.M., Ng, E.Y.K. and Laude, A., 2013. Computer-aided diagnosis of diabetic retinopathy: A review. Computers in biology and medicine, 43(12): 2136-2155.
Wankhade, N.R. and Bhoyar, K.K., 2021. Multi-Class Retinopathy classification in Fundus Image using Deep Learning Approaches. International Journal of Next-Generation Computing, 12(5).
Gulati, S., Singh, V.P. and Shukla, S., 2022, July. Comparative Analysis of Deep Learning Approaches for the Diagnosis of Diabetic Retinopathy. In 2022 IEEE Students Conference on Engineering and Systems (SCES) 1-6. IEEE.
Li, F., Wang, Y., Xu, T., Dong, L., Yan, L., Jiang, M., Zhang, X., Jiang, H., Wu, Z. and Zou, H., 2022. Deep learning-based automated detection for diabetic retinopathy and diabetic macular oedema in retinal fundus photographs. Eye, 36(7): 1433-1441.
Siddharth, D., Saini, D.K.J. and Singh, P., 2021. An Efficient Approach For Edge Detection Technique Using Kalman Filter With Artificial Neural Network. International Journal of Engineering. 34(12): 2604-2610.
