An Optimized Deep Learning based Technique for Grading and Extraction of Diabetic Retinopathy Severities
DOI:
https://doi.org/10.31449/inf.v45i5.3561Abstract
The prognosis of Diabetic Retinopathy (DR) requires regular eye examinations, as ophthalmologists depends on fundus segmentation to treat DR pathologies. Automated approaches for detection, segmentation and classification have developed as an imperative area of research for the effective diagnosis of DR for the treatment of serious eye conditions that prevent visual impairment. Diagnosis of various DR lesions, as well as different severities, helping the ophthalmologists to analyze variations in fundus images and take the necessary measures before the disease progresses. Deep learning techniques have evolved as a recent advent to combat the issues of conventional machine leaning based methods. An optimized deep learning framework is proposed in this article for grading and extraction of diabetic retinopathy severities. This involves various steps like background segmentation, feature set extraction, feature optimization using Cuckoo search and Convolutional Neural Network (CNN) severity grade classification. The method was validated on two standard datasets MESSIDOR and IDRiD. The proposed method yields an accuracy value of 97.55%, cross entropy loss of 0.367 and time intricacy of 20 mins and 15 secs for MESSIDOR and 98.02% cross entropy loss of 0.345 and time intricacy of 22 mins and 21 secs for IDRiD dataset; respectively. The state-of-the-art comparison depicts that the proposed CNN based method provides a maximum accuracy improvement of 10.46% comparative to the existing methodology. The proposed framework yields better accuracy by procurement of the investigative outcomes acquired exhibits proficient DR determination.References
Akram, M. U., Khalid, S., Tariq, A., Khan, S. A., & Azam, F. (2014). Detection and classification of retinal lesions for grading of diabetic retinopathy. Computers in biology and medicine, 45, 161-171. https://doi.org/10.1016/j.compbiomed.2013.11.014.
Fleming, A. D., Philip, S., Goatman, K. A., Olson, J. A., & Sharp, P. F. (2006). Automated microaneurysm detection using local contrast normalization and local vessel detection. IEEE transactions on medical imaging, 25(9), 1223-1232. https://doi.org/10.1109/tmi.2006.879953.
ElTanboly, A., Ghazal, M., Khalil, A., Shalaby, A., Mahmoud, A., Switala, A., ... & El-Baz, A. (2018, April). An integrated framework for automatic clinical assessment of diabetic retinopathy grade using spectral domain OCT images. In 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), pp. 1431-1435. IEEE. https://doi.org/10.1109/ISBI.2018.8363841.
Kumar, D., Taylor, G. W., & Wong, A. (2019). Discovery radiomics with CLEAR-DR: interpretable computer aided diagnosis of diabetic retinopathy. IEEE Access, 7, 25891-25896. https://doi.org/10.1109/ACCESS.2019.2893635.
ElTanboly, A., Ismail, M., Shalaby, A., Switala, A., El‐Baz, A., Schaal, S., ... & El‐Azab, M. (2017). A computer‐aided diagnostic system for detecting diabetic retinopathy in optical coherence tomography images. Medical physics, 44(3), 914-923. https://doi.org/10.1002/mp.12071.
Tariq, A., Akram, M. U., Shaukat, A., & Khan, S. A. (2013). Automated detection and grading of diabetic maculopathy in digital retinal images. Journal of digital imaging, 26(4), 803-812. https://doi.org/10.1007/s10278-012-9549-4.
Poddar, S., Jha, B. K., & Chakraborty, C. (2011, November). Quantitative clinical marker extraction from colour fundus images for non-proliferative diabetic retinopathy grading. In 2011 International Conference on Image Information Processing, pp. 1-6. IEEE. https://doi.org/10.1109/ICIIP.2011.6108956.
Fleming, A. D., Goatman, K. A., Philip, S., Williams, G. J., Prescott, G. J., Scotland, G. S., ... & Olson, J. A. (2010). The role of haemorrhage and exudate detection in automated grading of diabetic retinopathy. British Journal of Ophthalmology, 94(6), 706-711. https://doi.org/10.1136/bjo.2008.149807.
Li, X., Pang, T., Xiong, B., Liu, W., Liang, P., & Wang, T. (2017, October). Convolutional neural networks based transfer learning for diabetic retinopathy fundus image classification. In 2017 10th international congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI) (pp. 1-11). IEEE. https://doi.org/10.1109/CISP-BMEI.2017.8301998.
Lam, C., Yi, D., Guo, M., & Lindsey, T. (2018). Automated Detection of Diabetic Retinopathy using Deep Learning. AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2017, 147–155.
Perdomo, O., Otalora, S., Rodríguez, F., Arevalo, J., & González, F. A. (2016). A novel machine learning model based on exudate localization to detect diabetic macular edema. https://doi.org/110.17077/omia.1057.
Johari, M. H., Hassan, H. A., Yassin, A. I. M., Tahir, N. M., Zabidi, A., Rizman, Z. I., ... & Wahab, N. A. (2018). Early detection of diabetic retinopathy by using deep learning neural network. International Journal of Engineering and Technology (UAE), 7(4), 198-201. https://doi.org/10.14419/ijet.v7i4.11.20804.
Wang, Z., Yin, Y., Shi, J., Fang, W., Li, H., & Wang, X. (2017, September). Zoom-in-net: Deep mining lesions for diabetic retinopathy detection. In International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 267-275. Springer, Cham. arXiv:1706.04372.
Chen, Y. W., Wu, T. Y., Wong, W. H., & Lee, C. Y. (2018, April). Diabetic retinopathy detection based on deep convolutional neural networks. In 2018 IEEE international conference on acoustics, speech and signal processing (ICASSP), pp. 1030-1034. IEEE. https://doi.org/10.1109/ICASSP.2018.8461427.
Gonçalves, J., Conceiçao, T., & Soares, F. (2019). Inter-observer Reliability in Computer-aided Diagnosis of Diabetic Retinopathy. In HEALTHINF (pp. 481-491). https://doi.org/10.5220/0007580904810491.
Li, X., Hu, X., Yu, L., Zhu, L., Fu, C. W., & Heng, P. A. (2019). CANet: cross-disease attention network for joint diabetic retinopathy and diabetic macular edema grading. IEEE transactions on medical imaging, 39(5), 1483-1493. https://doi.org/10.1109/TMI.2019.2951844.
Bhardwaj, C., Jain, S., & Sood, M. (2021). Hierarchical severity grade classification of non-proliferative diabetic retinopathy. Journal of Ambient Intelligence and Humanized Computing, 12(2), 2649-2670. https://doi.org/10.1007/s12652-020-02426-9.
Saranya, P., & Prabakaran, S. (2020). Automatic detection of non-proliferative diabetic retinopathy in retinal fundus images using convolution neural network. Journal of Ambient Intelligence and Humanized Computing, 1-10. https://doi.org/10.1007/s12652-020-02518-6.
Dhiman, G., Oliva, D., Kaur, A., Singh, K. K., Vimal, S., Sharma, A., & Cengiz, K. (2021). BEPO: A novel binary emperor penguin optimizer for automatic feature selection. Knowledge-Based Systems, 211, 106560. https://doi.org/10.1016/j.knosys.2020.106560.
Dhiman, G., Singh, K. K., Soni, M., Nagar, A., Dehghani, M., Slowik, A., ... & Cengiz, K. (2021). MOSOA: a new multi-objective seagull optimization algorithm. Expert Systems with Applications, 167, 114150. https://doi.org/10.1016/j.eswa.2020.114150.
Bhardwaj, C., Jain, S., & Sood, M. (2021). Hierarchical severity grade classification of non-proliferative diabetic retinopathy. Journal of Ambient Intelligence and Humanized Computing, 12(2), 2649-2670. https://doi.org/10.1007/s12652-020-02426-9.
Bhardwaj, C., Jain, S., & Sood, M. (2020). Diabetic retinopathy lesion discriminative diagnostic system for retinal fundus images. Advanced Biomedical Engineering, 9, 71-82. https://doi.org/10.14326/abe.9.71.
Rathee, G., Sharma, A., Kumar, R., Ahmad, F., & Iqbal, R. (2020). A trust management scheme to secure mobile information centric networks. Computer Communications, 151, 66-75. https://doi.org/10.1016/j.comcom.2019.12.024.
Yuvaraj, N., Srihari, K., Dhiman, G., Somasundaram, K., Sharma, A., Rajeskannan, S., ... & Masud, M. (2021). Nature-Inspired-Based Approach for Automated Cyberbullying Classification on Multimedia Social Networking. Mathematical Problems in Engineering, 2021. https://doi.org/10.1155/2021/6644652.
Decencière, E., Zhang, X., Cazuguel, G., Lay, B., Cochener, B., Trone, C., ... & Klein, J. C. (2014). Feedback on a publicly distributed image database: the Messidor database. Image Analysis & Stereology, 33(3), 231-234. https://doi.org/10.5566/ias.1155.
Porwal, P., Pachade, S., Kamble, R., Kokare, M., Deshmukh, G., Sahasrabuddhe, V., & Meriaudeau, F. (2018). Indian diabetic retinopathy image dataset (IDRiD): a database for diabetic retinopathy screening research. Data, 3(3), 25. https://doi.org/10.3390/data3030025.
Mareli, M., & Twala, B. (2018). An adaptive Cuckoo search algorithm for optimisation. Applied computing and informatics, 14(2), 107-115. https://doi.org/ 10.1016/j.aci.2017.09.001.
Gao, Z., Li, J., Guo, J., Chen, Y., Yi, Z., & Zhong, J. (2018). Diagnosis of diabetic retinopathy using deep neural networks. IEEE Access, 7, 3360-3370. https://doi.org/10.1109/ACCESS.2018.2888639.
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 25, 1097-1105. https://doi.org/10.1145/3065386.
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., ... & Rabinovich, A. (2015). Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1-9. arXiv:1409.4842.
He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778). https://doi.org/10.1109/CVPR.2016.90.
Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
Vo, H. H., & Verma, A. (2016, December). New deep neural nets for fine-grained diabetic retinopathy recognition on hybrid color space. In 2016 IEEE International Symposium on Multimedia (ISM) (pp. 209-215). IEEE. https://doi.org/10.1109/ISM.2016.0049.
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