An optimized intelligent boosting model for diabetic retinopathy segmentation severity analysis using fundus images
Article Sidebar
Main Article Content
Abstract
In today's scenario, many people suffer from Diabetic Retinopathy (DR), due to different lifestyles and cultures. Hence, the exact severity analysis system is the most required application to avoid vision loss. The Neural network with multiple decision functions already existed for this severity analysis case. However, those models do not give the proper outcome in exact segmentation, leading to improper severity analysis outcomes. So, the current study aims to design a novel Squirrel Search-based Extreme Boosting (SSbEB) for accurately segmenting and estimating the severity range. Initially, the DR database was filtered and entered into the classification layer, then the features were extracted, and the abnormal region was segmented. Here, incorporating the squirrel features in the extreme boosting has afforded the finest feature analysis and segmentation outcome, which help predict the DR severity level with the maximum possible rate. The severity score of the segmented region was determined as normal, mild, severe, moderate, and proliferative. Hence, the designed model is implemented in the python platform, and the performance parameters, such as precision, specificity, accuracy, and recall, have been measured and compared with other models. Hence, the recorded exact severity analysis score is 94.4%, which is quite better than the past models. Thus, the implemented model is suitable for the DR severity analysis system and supported for real-time disease analysis applications.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
References
Karkuzhali S, Manimegalai D. Distinguising proof of diabetic retinopathy detection by hybrid approaches in two dimensional retinal fundus images. J Med Syst. 2019;43(6):173.
Karthikeyan R, Alli P. Feature selection and parameters optimization of support vector machines based on hybrid glowworm swarm optimization for classification of diabetic retinopathy. J Med Syst. 2018;42(10):195.
Maji D, Sekh AA. Automatic grading of retinal blood vessel in deep retinal image diagnosis. J Med Syst. 2020;44(10):180.
Qiao L, Zhu Y, Zhou H. Diabetic retinopathy detection using prognosis of microaneurysm and early diagnosis system for non-proliferative diabetic retinopathy based on deep learning algorithms. IEEE Access. 2020;8:104292-302.
Gadekallu TR, Khare N, Bhattacharya S, Singh S, Maddikunta PKR, Srivastava G. Deep neural networks to predict diabetic retinopathy. J Ambient Intell Human Comput. 2020:1-14.
Chiang M, Quinn GE, Fielder AR, Ostmo SR, Chan RVP, Berrocal A, et al. International classification of retinopathy of prematurity. Ophthalmology. 2021;128(10):E51-68.
Gargi M, Namburu A. Severity detection of diabetic retinopathy—a review. Int J Image Graph. 2020:2340007.
Zhang J, Li C, Rahaman MM, Yao Y, Ma P, Zhang J, et al. A comprehensive review of image analysis methods for microorganism counting: from classical image processing to deep learning approaches. Artif Intell Rev. 2022;55:2875-944.
Bora A, Balasubramanian S, Babenko B, Virmani S, Venugopalan S, Mitani A, et al. Predicting the risk of developing diabetic retinopathy using deep learning. Lancet Digit Health. 2021;3(1):E10-9.
Alyoubi WL, Shalash WM, Abulkhair MF. Diabetic retinopathy detection through deep learning techniques: a review. Inform Med Unlocked. 2020;20:100377.
Shankar K, Sait ARW, Gupta D, Lakshmanaprabu SK, Khanna A, Pandey HM. Automated detection and classification of fundus diabetic retinopathy images using synergic deep learning model. Pattern Recognit Lett. 2020;133:210-6.
Jampol LM, Glassman AR, Sun J. Evaluation and care of patients with diabetic retinopathy. N Engl J Med. 2020;382(17):1629-37.
Shankar K, Zhang Y, Liu Y, Wu L, Chen CH. Hyperparameter tuning deep learning for diabetic retinopathy fundus image classification. IEEE Access. 2020;8:118164-73.
Salvi M, Acharya UR, Molinari F, Meiburger KM. The impact of pre-and post-image processing techniques on deep learning frameworks: a comprehensive review for digital pathology image analysis. Comput Biol Med. 2021;128:104129.
Kaushik H, Singh D, Kaur M, Alshazly H, Zaguia A, Hamam H. Diabetic retinopathy diagnosis from fundus images using stacked generalization of deep models. IEEE Access. 2021;9:108276-92.
Veena HN, Muruganandham A, Kumaran TS. A novel optic disc and optic cup segmentation technique to diagnose glaucoma using deep learning convolutional neural network over retinal fundus images. J King Saud Univ-Comput Inf Sci. 2021;34(8):6187-98.
Tang S, Yu F. Construction and verification of retinal vessel segmentation algorithm for color fundus image under BP neural network model. J Supercomput. 2021;77(4):3870-84.
Cho H, Hwang YH, Chung JK, Lee KB, Park JS, Kim HG. Deep learning ensemble method for classifying glaucoma stages using fundus photographs and convolutional neural networks. Curr Eye Res. 2021;46(10):1516-24.
Imran A, Li J, Pei Y, Akhtar F, Mahmood T, Zhang L. Fundus image-based cataract classification using a hybrid convolutional and recurrent neural network. Vis Comput. 2021;37(8):2407-17.
Bhandari S, Pathak S, Jain SA. A literature review of early-stage diabetic retinopathy detection using deep learning and evolutionary computing techniques. Arch Computat Methods Eng. 2023:30:799-810.
Jena M, Mishra D, Mishra SP, Mallick PK. A tailored complex medical decision analysis model for diabetic retinopathy classification based on optimized un-supervised feature learning approach. Arab J Sci Eng. 2022;48:2087-99.
Liu Z. Construction and verification of color fundus image retinal vessels segmentation algorithm under BP neural network. J Supercomput. 2021;77(7):7171-83.
Liu Y, Sang M, Yuan Y, Du Z, Li W, Hu H, et al. Novel clusters of newly-diagnosed type 2 diabetes and their association with diabetic retinopathy: a 3-year follow-up study. Acta Diabetol. 2022;59(6):827-35.
Khan KB, Khaliq AA, Jalil A, Iftikhar MA, Ullah N, Aziz MW, et al. A review of retinal blood vessels extraction techniques: challenges, taxonomy, and future trends. Pattern Anal Applic. 2019;22:767-802.
Gomułka K, Ruta M. The role of inflammation and therapeutic concepts in diabetic retinopathy—a short review. Int J Mol Sci. 2023;24(2):1024.
Tang F, Luenam P, Ran AR, Quadeer AA, Raman R, Sen P, et al. Detection of diabetic retinopathy from ultra-widefield scanning laser ophthalmoscope images: a multicenter deep learning analysis. Ophthalmol Retina. 2021;5(11):1097-106.
Sevgi DD, Srivastava SK, Whitney J, Connell MO, Kar SS, Hu M, et al. Characterization of ultra-widefield angiographic vascular features in diabetic retinopathy with automated severity classification. Ophthalmol Sci. 2021;1(3):100049.
Campbell JP, Kim SJ, Brown JM, Ostmo S, Chan RVP, Kalpathy-Cramer J, et al. Evaluation of a deep learning–derived quantitative retinopathy of prematurity severity scale. Ophthalmology. 2021;128(7):1070-6.
Kaur J, Mittal D, Singla R. Diabetic retinopathy diagnosis through computer-aided fundus image analysis: a review. Arch Comput Methods Eng. 2022;29:1673-711.
Modjtahedi BS, Wu J, Luong TQ, Gandhi NK, Fong DS, Chen W. Severity of diabetic retinopathy and the risk of future cerebrovascular disease, cardiovascular disease, and all-cause mortality. Ophthalmology. 2021;128(8):1169-79.
Sikder N, Masud M, Bairagi AK, Arif ASM, Nahid AA, Alhumyani HA. Severity classification of diabetic retinopathy using an ensemble learning algorithm through analyzing retinal images. Symmetry 2021;13(4):670.
Rehman A, Harouni M, Karimi M, Saba T, Bahaj SA, Awan MJ. Microscopic retinal blood vessels detection and segmentation using support vector machine and K‐nearest neighbors. Microsc Res Tech. 2022;85(5):1899-914.
Jena PK, Khuntia B, Palai C, Nayak M, Mishra TK, Mohanty SN. A novel approach for diabetic retinopathy screening using asymmetric deep learning features. Big Data Cogn Comput. 2023;7(1):25.
Purna Chandra Reddy V, Gurrala KK. Machine learning and deep learning-based framework for detection and classification of diabetic retinopathy. In: Paunwala C, Paunwala M, Kher R, Thakkar F, Kher H, Atiquzzaman M, et al., editors. Biomedical Signal and Image Processing with Artificial Intelligence. Cham: Springer International Publishing; 2023. p. 271-86.
Wang X, Fang Y, Yang S, Zhu D, Wang M, Zhang J, et al. CLC-Net: Contextual and local collaborative network for lesion segmentation in diabetic retinopathy images. Neurocomputing. 2023;527:100-9.
Qiu R, Liu C, Cui N, Gao Y, Li L, Wu Z, et al. Generalized extreme gradient boosting model for predicting daily global solar radiation for locations without historical data. Energy Convers Manag. 2022;258:115488.
Deb D, Roy S. Brain tumor detection based on hybrid deep neural network in MRI by adaptive squirrel search optimization. Multimed Tools Appl. 2021;80(2):2621-45.
Bilal A, Sun G, Li Y, Mazhar S, Khan AQ. Diabetic retinopathy detection and classification using mixed models for a disease grading database. IEEE Access. 2021;9:23544-53.
Sivapriya G, Praveen V, Gowri P, Saranya S, Sweetha S, Shekar K. Segmentation of hard exudates for the detection of diabetic retinopathy with RNN based sematic features using fundus images. Mater Today: Proc. 2022;64:693-701.