Abstract
Diabetic Retinopathy (DR) stands as a primary cause of blindness across all age groups, attributed to insufficient blood supply to the retina, retinal vascular exudation, and intraocular hemorrhage. Despite recent strides in DR diagnosis and treatment, this complication remains a formidable challenge for both physicians and patients alike. Consequently, the demand for a comprehensive and automated DR screening approach has become imperative, aiming to achieve early detection and potentially revolutionize the management of this disease. This study introduces a novel approach for identifying diabetic retinopathy through transfer learning-based optical image data classification. We have proposed four methods based on pretrained models: VGG16, VGG19, InceptionV3, and DenseNet169. The effectiveness of the newly reformed networks is evaluated using four performance metrics, using the APTOS2019 dataset as the basis for validation. The results demonstrated that the InceptionV3 model achieved the highest accuracy of 96.88%. It outperformed all other state-of-the-art diabetic retinopathy detection models.
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Dataset Aptos 2019 used in this study, is publicly available at: https://www.kaggle.com/datasets/sovitrath/diabetic-retinopathy-224x224-2019-data
References
Allen, D.A., Yaqoob, M.M., Harwood, S.M.: Mechanisms of high glucose-induced apoptosis and its relationship to diabetic complications. J. Nutr. Biochem. 16, 705–713 (2005)
Rubsam, A., Parikh, S., Fort, P.E.: Role of Infammation in Diabetic Retinopathy. Int. J. Mol. Sci. 19, 942–973 (2018)
Faust, O., Rajendra Acharya, U., Ng, E.-K., Ng, K.-H., Suri, J.S.: Algorithms for the automated detection of diabetic retinopathy using digital fundus images: a review. J. Med. Syst. 36, 145–157 (2012)
Akram, M.U., Khalid, S., Khan, S.A.: Identification and classification of microaneurysms for early detection of diabetic retinopathy. Pattern Recogn. 46(1), 107–116 (2013)
Medhi, J.P., Dandapat, S.: An effective fovea detection and automatic assessment of diabetic maculopathy in color fundus images. Comput. Biol. Med. 74, 30–44 (2016)
Pratt H, Coenen F, Broadbent DM, Harding SP, Zheng Y (2016) Convolutional neural networks for diabetic retinopathy. Proc Comput Sci 90:200–205 (20th Conference on Medical Image Understanding and Analysis (MIUA 2016))
Quellec, G., Charrière, K., Boudi, Y., Cochener, B., Lamard, M.: Deep image mining for diabetic retinopathy screening. Med. Image Anal. 39,178–193 (2017)
James, J., Sharifahmadian, E., Shih, L.: Automatic severity level classification of diabetic retinopathy. Int. J. Comput. Appl. 180, 30–35 (2018)
Yang, Y., Li, T., Li, W., Wu, H., Fan, W., Zhang, W.: Lesion detection and grading of diabetic retinopathy via two-stages deep convolutional neural networks. In: MICCAI (2017)
Li, H., Wang, Y., Wang, H., Zhou, B.: Multi-window based ensemble learning for classification of imbalanced streaming data. World Wide Web 20, 1–19 (2017)
Peng, M., Zeng, G., Sun, Z., Huang, J., Wang, H., Tian, G.: Personalized app recommendation based on app permissions. World Wide Web 21, 1–16 (2018)
Nair, M., Mishra, D.S.: Categorization of diabetic retinopathy severity levels of transformed images using clustering approach. Int. J. Comput. Sci. Eng. 7(1), 642–648 (2019)
Nair, M., Mishra, D.: Classification of diabetic retinopathy severity levels of transformed images using K-means and thresholding method. Int. J. Eng. Adv. Technol. 8(4), 51–59 (2019)
Ghosh, R.: Automatic detection and classification of diabetic retinopathy stages using CNN. In: Proceedings of the 2017 4th International Conference on Signal Processing and Integrated Networks (SPIN), Noida, Delhi-NCR, India, 2–3 February 2017
Ruamviboonsuk, P., Krause, J., Chotcomwongse, P., et al.: Deep learning versus human graders for classifying diabetic retinopathy severity in a nationwide screening program. NPJ. Digit. Med. 2(1), 1–9 (2019)
Hemanth, D.J., Deperlioglu, O., Kose, U.: An enhanced diabetic retinopathy detection and classification approach using deep convolutional neural network. Neural Comput Applic 32(3):707–721Return to ref 24 in article (2020)
Wan, S., Liang, Y., Zhang, Y.: Deep convolutional neural networks for diabetic retinopathy detection by image classification. Comput. Electr. Eng. 72, 274–282 (2018)
Islam, M.M., Yang, H.C., Poly, T.N., Jian, W.S., Li, Y.C.: Deep learning algorithms for detection of diabetic retinopathy in retinal fundus photographs: a systematic review and meta-analysis. Comput Methods Prog Biomed 191, 105320 (2020)
Kathiresan, S., Sait, A.R.W., Gupta, D., Lakshmanaprabu, S.K., Khanna, A., Pandey, H.M.: Automated detection and classification of fundus diabetic retinopathy images using synergic deep learning model. Pattern Recogn. Lett. 133, 210–216 (2020)
https://www.kaggle.com/datasets/sovitrath/diabetic-retinopathy-224x224-2019-data
Bodapati, J.D., Shaik, N.S., Naralasetti, V.: Deep convolution feature aggregation: an application to diabetic retinopathy severity level prediction. Signal Image Video Process. 15, 923–930 (2021)
Gupta, S., Thakur, S., Gupta, A.: Optimized hybrid machine learning approach for smartphone based diabetic retinopathy detection. Multimed. Tools Appl. 81, 14475–14501 (2022). https://doi.org/10.1007/s11042-022-12103-y
Nahiduzzaman, Md., Robiul Islam, Md., Omaer Faruq Goni, Md., Shamim Anower, Md., Ahsan, M., Haider, J., Kowalski, M.: Diabetic retinopathy identification using parallel convolutional neural network based feature extractor and ELM classifier, Expert Systems with Applications, vol 217 (2023): 119557, ISSN 0957-4174. https://doi.org/10.1016/j.eswa.2023.119557
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F.K, A.E. contributed to Methodology; A.E. contributed to Project administration; F.K. contributed to Writing original draft; A.E.; and F.K contributed to Writing—review & editing. All authors have read and agreed to the published version of the manuscript.
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Kallel, F., Echtioui, A. Retinal fundus image classification for diabetic retinopathy using transfer learning technique. SIViP 18, 1143–1153 (2024). https://doi.org/10.1007/s11760-023-02820-8
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DOI: https://doi.org/10.1007/s11760-023-02820-8