Prediction of Diabetic Eye Disease in Type 2 Diabetes Mellitus using Deep Learning based Approaches: A Comprehensive Analysis
Keywords:
Diabetic Eye Disease, EfficientNet121, NASNetLarge, ResNet50, VGG16, VGG19Abstract
Diabetic Eye Disease occurs when blood vessels linked to light-sensitive tissue existing in the retina of the eye are damaged. Furthermore, based on the severity level of the disease, it can lead to full blindness and a variety of other visual problems. The present research work is based on the analysis of various Deep Neural Networks (DNN) that are applied on a dataset consisting of retinal images for the prediction of eye disease especially found in type-2 diabetic patients. This study validates that deep learning-based models such as Visual Geometric Group16 (VGG16), Visual Geometric Group 19 (VGG19), EfficientNetwork121(EfficientNet121), Residual Neural Network50(ResNet50), and Neural Architecture Search Network Large (NASNetLarge) can predict diabetic eye disease. Several image feature extraction techniques (Contour Feature Description, Segmentation, Color Conversion from BGR to RGB, Gaussian Blur, and Cropping) are used for the feature extraction of color retinal images. The dataset comprised 135930 training images whereas 45310 validation images fitted in five DR types such as No DR, Mild, Moderate, Severe and Proliferative, as a result of data split in ratios of 75% (train) and 25% (test). The accuracy based on training data is compared for all classification models considered in this research work and it has been observed VGG16 gives the highest accuracy. Similarly, the training data accuracy of other models used in this work is also considered (between 85%-99%). Likewise, VGG19 and VGG16 both had high validation data accuracy such as 89.01% and 88.27%, respectively, but ResNet50 had the lowest validation data accuracy of 89.01%.
Downloads
References
Marques, I. P., Madeira, M. H., Messias, A. L., Martinho, A. C. V., Santos, T., Sousa, D. C., & Cunha-Vaz, J. (2021). Different retinopathy phenotypes in type 2 diabetes predict retinopathy progression. Acta Diabetologica, 58(2), 197-205.
Hsiao, Y. T., Shen, F. C., Weng, S. W., Wang, P. W., Chen, Y. J., & Lee, J. J. (2021). Multiple Single Nucleotide Polymorphism Testing Improves the Prediction of Diabetic Retinopathy Risk with Type 2 Diabetes Mellitus. Journal of personalized medicine, 11(8), 689.
Smith, J. J., Wright, D. M., Stratton, I. M., Scanlon, P. H., & Lois, N. (2021). Testing the performance of risk prediction models to determine progression to referable diabetic retinopathy in an Irish type 2 diabetes cohort.
Al-Turk, L., Wawrzynski, J., Wang, S., Krause, P., Saleh, G. M., Alsawadi, H., ... & Tang, H. L. (2021). Automated feature-based grading and progression analysis of diabetic retinopathy. Eye, 1-9.
Reguant, R., Brunak, S., & Saha, S. (2021). Understanding inherent image features in CNN-based assessment of diabetic retinopathy. Scientific Reports, 11(1), 1-12.
Rani, M. J., Shanthi, T., & Kaviya, P. (2021). Diagnosis of Diabetic Retinopathy from Fundal Photography. Annals of the Romanian Society for Cell Biology, 25(6), 4526-4538.
Shen, Z., Wu, Q., Wang, Z., Chen, G., & Lin, B. (2021). Diabetic Retinopathy Prediction by Ensemble Learning Based on Biochemical and Physical Data. Sensors, 21(11), 3663.
Reddy, S. S., Sethi, N., & Rajender, R. (2021). Discovering Optimal Algorithm to Predict Diabetic Retinopathy using Novel Assessment Methods. EAI Endorsed Transactions on Scalable Information Systems, 8(29), e1.
Bora, A., Balasubramanian, S., Babenko, B., Virmani, S., Venugopalan, S., Mitani, A., ... & Bavishi, P. (2021). Predicting the risk of developing diabetic retinopathy using deep learning. The Lancet Digital Health, 3(1), e10- e19.
Tejas, S., Rachana, S., & Tejaswini, R. Prediction of Cataract and Diabetic Retinopathy using Deep Neural Network.Science and Engineering Journal, Volume 25, Issue 7 2021.
Thomas, G., Robinson, Y. H., Julie, E. G., Shanmuganathan, V., Rho, S., & Nam, Y. (2021). Intelligent prediction approach for diabetic retinopathy using deep learning-based convolutional neural networks algorithm by means of retina photographs.
Liu, P., Yang, X., Jin, B., & Zhou, Q. (2021). Diabetic Retinal Grading Using Attention-Based Bilinear Convolutional Neural Network and Complement Cross Entropy. Entropy, 23(7), 816.
Kim, K. M., Heo, T. Y., Kim, A., Kim, J., Han, K. J., Yun, J., & Min, J. K. (2021). Development of a Fundus Image-Based Deep Learning Diagnostic Tool for Various Retinal Diseases. Journal of Personalized Medicine, 11(5), 321.
Kaushik, H., Singh, D., Kaur, M., Alshazly, H., Zaguia, A., & Hamam, H. (2021). Diabetic retinopathy diagnosis from fundus images using stacked generalization of deep models. IEEE Access, 9, 108276-108292.
Chen, P. N., Lee, C. C., Liang, C. M., Pao, S. I., Huang, K. H., & Lin, K. F. (2021). General deep learning model for detecting diabetic retinopathy. BMC Bioinformatics, 22(5), 1-15.
Bodapati, J. D., Shaik, N. S., & Naralasetti, V. (2021). Composite deep neural network with a gated-attention mechanism for diabetic retinopathy severity classification. Journal of Ambient Intelligence and Humanized Computing, 1-15.
Bodapati, J. D., Naralasetti, V., Shareef, S. N., Hakak, S., Bilal, M., Maddikunta, P. K. R., & Jo, O. (2020). Blended multi-modal deep convnet features for diabetic retinopathy severity prediction. Electronics, 9(6), 914.
Bodapati, J. D., Shaik, N. S., Naralasetti, V., & Mundukur, N. B. (2021). Joint training of two-channel deep neural network for brain tumor classification. Signal, Image and Video Processing, 15(4), 753-760.
Deepika, K., Bodapati, J. D., & Srihitha, R. K. (2019). An efficient automatic brain tumor classification using LBP features and SVM-based classifier. In Proceedings of International Conference on Computational Intelligence and Data Engineering (pp. 163-170). Springer, Singapore.
Zeng, X., Chen, H., Luo, Y., & Ye, W. (2019). Automated diabetic retinopathy detection based on binocular siamese-like convolutional neural network. IEEE Access, 7, 30744-30753.
Dutta, S., Manideep, B. C., Basha, S. M., Caytiles, R. D., & Iyengar, N. C. S. N. (2018). Classification of diabetic retinopathy images by using deep learning models. International Journal of Grid and Distributed Computing, 11(1), 89-106.
Shaban, M., Ogur, Z., Mahmoud, A., Switala, A., Shalaby, A., Abu Khalifeh, H., ... & El-Baz, A. S. (2020). A convolutional neural network for the screening and staging of diabetic retinopathy. Plos one, 15(6), e0233514.
Porter, L. F., Saptarshi, N., Fang, Y., Rathi, S., Den Hollander, A. I., De Jong, E. K., ... & Paraoan, L. (2019). Whole-genome methylation profiling of the retinal pigment epithelium of individuals with age-related macular degeneration reveals differential methylation of the SKI, GTF2H4, and TNXB genes. Clinical epigenetics, 11(1), 1-14.
Deng, J., Dong, W., Socher, R., Li, L. J., Li, K., & Fei-Fei, L. (2009, June). Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition (pp. 248-255). Ieee.
Mateen, M., Wen, J., Song, S., & Huang, Z. (2019). Fundus image classification using VGG-19 architecture with PCA and SVD. Symmetry, 11(1), 1.
Dondeti, V., Bodapati, J. D., Shareef, S. N., & Veeranjaneyulu, N. (2020). Deep Convolution Features in Non-linear Embedding Space for Fundus Image Classification. Rev. d'Intelligence Artif., 34(3), 307-313.
Kassani, S. H., Kassani, P. H., Khazaeinezhad, R., Wesolowski, M. J., Schneider, K. A., & Deters, R. (2019, December). Diabetic retinopathy classification using a modified xception architecture. In 2019 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT) (pp. 1-6). IEEE.
Bodapati, J. D., Naralasetti, V., Shareef, S. N., Hakak, S., Bilal, M., Maddikunta, P. K. R., & Jo, O. (2020). Blended multi-modal deep convnet features for diabetic retinopathy severity prediction. Electronics, 9(6), 914.
Antal, B., & Hajdu, A. (2014). An ensemble-based system for automatic screening of diabetic retinopathy. Knowledge-based systems, 60, 20-27.
Gargeya, R., & Leng, T. (2017). Automated identification of diabetic retinopathy using deep learning. Ophthalmology, 124(7), 962-969.
Early Treatment Diabetic Retinopathy Study Research Group. (1991). Early photocoagulation for diabetic retinopathy: ETDRS report number 9. Ophthalmology, 98(5), 766-785.
Gadkari, S. S., Maskati, Q. B., & Nayak, B. K. (2016). Prevalence of diabetic retinopathy in India: The all India ophthalmological society diabetic retinopathy eye screening study 2014. Indian journal of ophthalmology, 64(1), 38.
Abràmoff, M. D., Lavin, P. T., Birch, M., Shah, N., & Folk, J. C. (2018). Pivotal trial of an autonomous AI- based diagnostic system for detection of diabetic retinopathy in primary care offices. NPJ digital medicine, 1(1), 1-8.
Ting, D. S., Peng, L., Varadarajan, A. V., Keane, P. A., Burlina, P. M., Chiang, M. F., ... & Wong, T. Y. (2019). Deep learning in ophthalmology: the technical and clinical considerations. Progress in retinal and eye research, 72, 100759.
Bellemo, V., Lim, G., Rim, T. H., Tan, G. S., Cheung, C. Y., Sadda, S., ... & Ting, D. S. W. (2019). Artificial intelligence screening for diabetic retinopathy: the real-world emerging application. Current diabetes reports, 19(9), 1-12.
Tan, M., & Le, Q. (2019, May). Efficientnet: Rethinking model scaling for convolutional neural networks. In the International conference on machine learning (pp. 6105-6114). PMLR.
Zoph, B., Vasudevan, V., Shlens, J., & Le, Q. V. (2018). Learning transferable architectures for scalable image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 8697- 8710).
Albahli, S., & Albattah, W. (2020). Detection of coronavirus disease from X-ray images using deep learning and transfer learning algorithms. Journal of X-ray Science and Technology, 28(5), 841-850.
https://www.kaggle.com/c/diabetic-retinopathy-detection/data
https://www.kaggle.com/makrovh/diabetic/retinopathy-blindness-detection-c-data
Gangwar, A. K., & Ravi, V. (2021). Diabetic retinopathy detection using transfer learning and deep learning. In Evolution in Computational Intelligence (pp. 679-689). Springer, Singapore.
Qiao, L., Zhu, Y., & Zhou, H. (2020). Diabetic retinopathy detection using prognosis of microaneurysm and early diagnosis system for non-proliferative diabetic retinopathy based on deep learning algorithms. IEEE Access, 8, 104292-10
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
