Abstract
This paper presents an automatic diabetes Retinopathy (DR) detection system using fundus images. The proposed automatic DR screening model saves the time of the ophthalmologist in disease diagnosis. In this approach, the segmentation is conducted using an improved watershed algorithm and Gray Level Co-occurrence Matrix (GLCM) is used for feature extraction. An improved Ensemble Extreme Learning Machine (EELM) is used for classification and its weights are tuned using the Crystal Structure Algorithm (CRYSTAL) algorithm which also optimizes the loss function of the EELM classifier. The experiments are conducted using two datasets namely DRIVE and MESSIDOR by comparing the proposed approach against different state-of-art techniques such as Support Vector Machine, VGG19, Ensemble classifier, and Synergic Deep Learning model. When compared to existing methodologies, the proposed approach has sensitivity, specificity, and accuracy scores of 97%, 97.3%, and 98%, respectively.
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References
Kadan, A.B., Subbian, P.S.: Diabetic retinopathy detection from fundus images using machine learning techniques: a review. Wirel. Pers. Commun. 121(3), 2199–2212 (2021)
Akram, M.U., Akbar, S., Hassan, T., Khawaja, S.G., Yasin, U., Basit, I.: Data on fundus images for vessels segmentation, detection of hypertensive retinopathy, diabetic retinopathy and papilledema. Data Brief 29, 105282 (2020)
Tsiknakis, N., et al.: Deep learning for diabetic retinopathy detection and classification based on fundus images: a review. Comput. Biol. Med. 135, 104599 (2021)
Ravishankar, S., Jain, A., Mittal, A.: Automated feature extraction for early detection of diabetic retinopathy in fundus images. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 210–217. IEEE, June 2009
Bonaccorso, G.: Machine Learning Algorithms: Popular Algorithms for Data Science and Machine Learning. Packt Publishing Ltd. (2018)
Dai, L., et al.: Clinical report guided retinal microaneurysm detection with multi-sieving deep learning. IEEE Trans. Med. Imaging 37(5), 1149–1161 (2018)
Fu, H., et al.: Evaluation of retinal image quality assessment networks in different color-spaces. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11764, pp. 48–56. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32239-7_6
Saranya, P., Prabakaran, S.: Automatic detection of non-proliferative diabetic retinopathy in retinal fundus images using convolution neural network. J. Ambient Intell. Human Comput. (2020). https://doi.org/10.1007/s12652-020-02518-6
Kanimozhi, J., Vasuki, P., Roomi, S.M.M.: Fundus image lesion detection algorithm for diabetic retinopathy screening. J. Ambient Intell. Humaniz. Comput. 12(7), 7407–7416 (2020). https://doi.org/10.1007/s12652-020-02417-w
Dutta, A., Agarwal, P., Mittal, A., Khandelwal, S.: Detecting grades of diabetic retinopathy by extraction of retinal lesions using digital fundus images. Res. Biomed. Eng. 37(4), 641–656 (2021)
Melo, T., Mendonça, A.M., Campilho, A.: Microaneurysm detection in color eye fundus images for diabetic retinopathy screening. Comput. Biol. Med. 126, 103995 (2020)
Shankar, K., 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)
Katada, Y., Ozawa, N., Masayoshi, K., Ofuji, Y., Tsubota, K., Kurihara, T.: Automatic screening for diabetic retinopathy in interracial fundus images using artificial intelligence. Intell. Based Med. 3, 100024 (2020)
Pachiyappan, A., Das, U.N., Murthy, T.V., Tatavarti, R.: Automated diagnosis of diabetic retinopathy and glaucoma using fundus and OCT images. Lipids Health Dis. 11(1), 1–10 (2012)
Talatahari, S., Azizi, M., Tolouei, M., Talatahari, B., Sareh, P.: Crystal structure algorithm (CryStAl): a metaheuristic optimization method. IEEE Access 9, 71244–71261 (2021)
Park, Y., Guldmann, J.M.: Measuring continuous landscape patterns with gray-level co-occurrence matrix (GLCM) indices: an alternative to patch metrics? Ecol. Ind. 109, 105802 (2020)
Zhang, L., Zou, L., Wu, C., Jia, J., Chen, J.: Method of famous tea sprout identification and segmentation based on improved watershed algorithm. Comput. Electron. Agric. 184, 106108 (2021)
Dubey, V., Katarya, R.: Adaptive histogram equalization based approach for SAR image enhancement: a comparative analysis. In: 2021 5th International Conference on Intelligent Computing and Control Systems (ICICCS), pp. 878–883. IEEE, May 2021
Niemeijer, J.S., Ginneken, B., Loog, M., Abramoff, M.: Digital retinal images for vessel extraction (2007)
Sahani, M., Swain, B.K., Dash, P.K.: FPGA-based favourite skin colour restoration using improved histogram equalization with variable enhancement degree and ensemble extreme learning machine. IET Image Process. 15, 1247–1259 (2021)
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Vinayaki, V.D., Kalaiselvi, R. (2022). An Improved Ensemble Extreme Learning Machine Classifier for Detecting Diabetic Retinopathy in Fundus Images. In: Kalinathan, L., R., P., Kanmani, M., S., M. (eds) Computational Intelligence in Data Science. ICCIDS 2022. IFIP Advances in Information and Communication Technology, vol 654. Springer, Cham. https://doi.org/10.1007/978-3-031-16364-7_26
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DOI: https://doi.org/10.1007/978-3-031-16364-7_26
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