Ong, K. L. et al. Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050: A systematic analysis for the Global Burden of Disease Study 2021. Lancet402, 203–234 (2023).
Google Scholar
Teo, Z. L. et al. Global prevalence of diabetic retinopathy and projection of burden through 2045: Systematic review and meta-analysis. Ophthalmology128, 1580–1591 (2021).
Google Scholar
Thomas, R., Halim, S., Gurudas, S., Sivaprasad, S. & Owens, D. Idf diabetes atlas: A review of studies utilising retinal photography on the global prevalence of diabetes related retinopathy between 2015 and 2018. Diabetes Res. Clin. Pract.157, 107840 (2019).
Google Scholar
Zegeye, A. F., Temachu, Y. Z. & Mekonnen, C. K. Prevalence and factors associated with diabetes retinopathy among type 2 diabetic patients at Northwest Amhara Comprehensive Specialized Hospitals, Northwest Ethiopia 2021. BMC Ophthalmol.23, 9 (2023).
Google Scholar
Senapati, A., Tripathy, H. K., Sharma, V. & Gandomi, A. H. Artificial intelligence for diabetic retinopathy detection: A systematic review. Inform. Med. Unlocked45, 101445. https://doi.org/10.1016/j.imu.2024.101445 (2024).
Google Scholar
Ksiażek, W., Gandor, M. & Pławiak, P. Comparison of various approaches to combine logistic regression with genetic algorithms in survival prediction of hepatocellular carcinoma. Comput. Biol. Medicine 134, (2021).
Khanna, M., Singh, L. K., Shrivastava, K. & singh, R. An enhanced and efficient approach for feature selection for chronic human disease prediction: A breast cancer study. Heliyon10, e26799. https://doi.org/10.1016/j.heliyon.2024.e26799 (2024).
Google Scholar
Ksiażek, W. Explainable thyroid cancer diagnosis through two-level machine learning optimization with an improved naked mole-rat algorithm. Cancers 16, 4128. https://doi.org/10.3390/cancers16244128 (2024).
Google Scholar
Pławiak, P. & Acharya, U. R. Novel deep genetic ensemble of classifiers for arrhythmia detection using ecg signals. Neural Comput. Appl. 32, 11137–11161. https://doi.org/10.1007/s00521-018-03980-2 (2020).
Google Scholar
Ruiz de Miras, J., Ibáñez-Molina, A., Soriano, M. & Iglesias-Parro, S. Schizophrenia classification using machine learning on resting state eeg signal. Biomed. Signal Process. Control. 79, (2023).
Brunese, L., Martinelli, F., Mercaldo, F. & Santone, A. Machine learning for coronavirus covid-19 detection from chest x-rays. Procedia Comput. Sci.176, 2212–2221 (2020).
Google Scholar
Zhao, W., Jiang, W. & Qiu, X. Deep learning for COVID-19 detection based on CT images. Sci. Rep.https://doi.org/10.1038/s41598-021-93832-2 (2021).
Google Scholar
Singh, L. K., Khanna, M. & Garg, H. Multimodal biometric based on fusion of ridge features with minutiae features and face features. Int. J. Inf. Syst. Model. Des.11, 37–57. https://doi.org/10.4018/ijismd.2020010103 (2020).
Google Scholar
Debele, G. R. et al. Incidence of Diabetic Retinopathy and Its Predictors Among Newly Diagnosed Type 1 and Type 2 Diabetic Patients: A Retrospective Follow-up Study at Tertiary Health-care Setting of Ethiopia. Diabetes Metab Syndr Obes 14, 1305–1313 (2021).
Google Scholar
Osęka, M., Jamrozy-Witkowska, A. & Mulak, M. Selected problems and challenges of ophthalmic care system in Poland. OphthaTherapy.8, 100–106. https://doi.org/10.24292/01.ot.300321.2 (2021).
Google Scholar
Seoud, L., Chelbi, J. & Cheriet, F. Automatic grading of diabetic retinopathy on a public database (2015).
Pratt, H., Coenen, F., Broadbent, D. M., Harding, S. P. & Zheng, Y. Convolutional neural networks for diabetic retinopathy. Procedia Comput. Sci.90, 200–205 (2016).
Google Scholar
Acharya, U. R., Lim, C. M., Ng, E. Y. K., Chee, C. & Tamura, T. Computer-based detection of diabetes retinopathy stages using digital fundus images. Proc. Inst. Mech. Eng. Part H J. Eng. Med.223, 545–553 (2009).
Google Scholar
Mansour, R. F. Deep-learning-based automatic computer-aided diagnosis system for diabetic retinopathy. Biomed. Eng. Lett.8, 41–57. https://doi.org/10.1007/s13534-017-0047-y (2017).
Google Scholar
Li, F. et al. Automatic detection of diabetic retinopathy in retinal fundus photographs based on deep learning algorithm. Transl. Vis. Sci. Technol. 8, 4. https://doi.org/10.1167/tvst.8.6.4 (2019).
Google Scholar
Gangwar, A. K. spsampsps Ravi, V. Diabetic Retinopathy Detection Using Transfer Learning and Deep Learning, 679–689 (Springer Singapore, 2020).
Nahiduzzaman, M. et al. Diabetic retinopathy identification using parallel convolutional neural network based feature extractor and elm classifier. Expert Syst. Appl.217, 119557. https://doi.org/10.1016/j.eswa.2023.119557 (2023).
Google Scholar
Kassani, S. H. et al. Diabetic retinopathy classification using a modified xception architecture. In 2019 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), https://doi.org/10.1109/isspit47144.2019.9001846 (IEEE, 2019).
Wahab Sait, A. R. A lightweight diabetic retinopathy detection model using a deep-learning technique. Diagnostics 13, 3120. https://doi.org/10.3390/diagnostics13193120 (2023).
Google Scholar
Canayaz, M. Classification of diabetic retinopathy with feature selection over deep features using nature-inspired wrapper methods. Appl. Soft Comput.128, 109462. https://doi.org/10.1016/j.asoc.2022.109462 (2022).
Google Scholar
Qummar, S. et al. A deep learning ensemble approach for diabetic retinopathy detection. IEEE Access 7, 150530–150539. https://doi.org/10.1109/access.2019.2947484 (2019).
Google Scholar
Modi, P. & Kumar, Y. Smart detection and diagnosis of diabetic retinopathy using bat based feature selection algorithm and deep forest technique. Comput. Ind. Eng.182, 109364. https://doi.org/10.1016/j.cie.2023.109364 (2023).
Google Scholar
Hardas, M., Mathur, S., Bhaskar, A. & Kalla, M. Retinal fundus image classification for diabetic retinopathy using svm predictions. Phys. Eng. Sci. Med.45, 781–791. https://doi.org/10.1007/s13246-022-01143-1 (2022).
Google Scholar
Singh, L. K., Garg, H. spsampsps Pooja. Automated Glaucoma Type Identification Using Machine Learning or Deep Learning Techniques, 241–263 (Springer Singapore, 2019).
Singh, L. K., Pooja & Garg, H. Detection of glaucoma in retinal fundus images using fast fuzzy c means clustering approach. In 2019 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS), 397–403, https://doi.org/10.1109/icccis48478.2019.8974539 (IEEE, 2019).
Singh, L. K., Pooja, & Garg, H. Detection of glaucoma in retinal images based on multiobjective approach. Int. J. Appl. Evol. Comput.11, 15–27. https://doi.org/10.4018/ijaec.2020040102 (2020).
Google Scholar
Singh, L. K., Khanna, M., Thawkar, S. & Singh, R. A novel hybridized feature selection strategy for the effective prediction of glaucoma in retinal fundus images. Multimedia Tools Appl.83, 46087–46159. https://doi.org/10.1007/s11042-023-17081-3 (2023).
Google Scholar
Aptos dataset. https://www.kaggle.com/competitions/aptos2019-blindness-detection/data [Accessed: (17.12.2023)].
Aria dataset. https://paperswithcode.com/dataset/aria/ [Accessed: (17.12.2023)].
Drive dataset. https://www.kaggle.com/datasets/andrewmvd/drive-digital-retinal-images-for-vessel-extraction [Accessed: (17.12.2023)].
Eoptha dataset. https://www.adcis.net/en/third-party/e-ophtha/ [Accessed: (17.12.2023)].
Eye diseases classification kaggle dataset. https://www.kaggle.com/datasets/gunavenkatdoddi/eye-diseases-classification [Accessed: (17.12.2023)].
Hrf dataset. https://www5.cs.fau.de/research/data/fundus-images/ [Accessed: (17.12.2023)].
Idrid dataset. https://ieee-dataport.org/open-access/indian-diabetic-retinopathy-image-dataset-idrid [Accessed: (17.12.2023)].
Odir dataset. https://www.kaggle.com/datasets/andrewmvd/ocular-disease-recognition-odir5k [Accessed: (17.12.2023)].
Telediagnostics in ophthalmological examinations (2023).
Hayati, M. et al. Impact of CLAHE-based image enhancement for diabetic retinopathy classification through deep learning. Procedia Comput. Sci.216, 57–66. https://doi.org/10.1016/j.procs.2022.12.111 (2023).
Google Scholar
Azzopardi, G. & Azzopardi, N. Trainable cosfire filters for keypoint detection and pattern recognition. IEEE Trans. Pattern Anal. Mach. Intell.35, 490–503. https://doi.org/10.1109/tpami.2012.106 (2013).
Google Scholar
Zhu, X. & Rangayyan, R. M. Detection of the optic disc in images of the retina using the hough transform. In 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, https://doi.org/10.1109/iembs.2008.4649971 (IEEE, 2008).
Li, W. et al. Retinal vessel segmentation based on B-cosfire filters in fundus images. Front. Public Healthhttps://doi.org/10.3389/fpubh.2022.914973 (2022).
Google Scholar
Kumar, V., Biswas, S., Rajput, D. S., Patel, H. & Tiwari, B. Pca-based incremental extreme learning machine (pca-ielm) for covid-19 patient diagnosis using chest x-ray images. Comput. Intell. Neurosci. 2022, 1–17. https://doi.org/10.1155/2022/9107430 (2022).
Google Scholar
Kaplan, K., Kaya, Y., Kuncan, M. & Ertunç, H. M. Brain tumor classification using modified local binary patterns (lbp) feature extraction methods. Med. Hypotheses139, 109696. https://doi.org/10.1016/j.mehy.2020.109696 (2020).
Google Scholar
Nyasulu, C. et al. A comparative study of machine learning-based classification of tomato fungal diseases: Application of GLCM texture features. Heliyon9, e21697. https://doi.org/10.1016/j.heliyon.2023.e21697 (2023).
Google Scholar
Murugan, A., Nair, S. A. H., Preethi, A. A. P. & Kumar, K. P. S. Diagnosis of skin cancer using machine learning techniques. Microprocess. Microsyst.81, 103727. https://doi.org/10.1016/j.micpro.2020.103727 (2021).
Google Scholar
Najjar, F. H. et al. Histogram features extraction for edge detection approach. In 2022 5th International Conference on Engineering Technology and its Applications (IICETA), 373–378, https://doi.org/10.1109/IICETA54559.2022.9888697 (2022).
Breiman, L. Machine Learning 45, 5–32. https://doi.org/10.1023/a:1010933404324 (2001).
Chen, T. & Guestrin, C. Xgboost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’16, https://doi.org/10.1145/2939672.2939785 (ACM, 2016).
Nopour, R. Prediction of 12-month recurrence of pancreatic cancer using machine learning and prognostic factors. BMC Med. Inform. Decis. Mak.https://doi.org/10.1186/s12911-024-02766-y (2024).
Google Scholar
K, A. et al. Effect of multi filters in glucoma detection using random forest classifier. Meas. Sens.25, 100566. https://doi.org/10.1016/j.measen.2022.100566 (2023).
Google Scholar
Bader, M., Abdelwanis, M., Maalouf, M. & Jelinek, H. F. Detecting depression severity using weighted random forest and oxidative stress biomarkers. Sci. Rep.https://doi.org/10.1038/s41598-024-67251-y (2024).
Google Scholar
Islam, T. et al. Predictive modeling for breast cancer classification in the context of Bangladeshi patients by use of machine learning approach with explainable ai. Sci. Rep.https://doi.org/10.1038/s41598-024-57740-5 (2024).
Google Scholar
Mochurad, L., Babii, V., Boliubash, Y. & Mochurad, Y. Improving stroke risk prediction by integrating xgboost, optimized principal component analysis, and explainable artificial intelligence. BMC Med. Inform. Decis. Mak.https://doi.org/10.1186/s12911-025-02894-z (2025).
Google Scholar
Akiba, T., Sano, S., Yanase, T., Ohta, T. & Koyama, M. Optuna: A next-generation hyperparameter optimization framework. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019).
Pedregosa, F. et al. Scikit-learn: Machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).
Google Scholar
Chen, T. & Guestrin, C. XGBoost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’16, 785–794, https://doi.org/10.1145/2939672.2939785 (ACM, New York, NY, USA, 2016).
Akiba, T., Sano, S., Yanase, T., Ohta, T. & Koyama, M. Optuna: A next-generation hyperparameter optimization framework. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019).
van der Walt, S. et al. Scikit-image: Image processing in python. PeerJ2, e453 (2014).
Google Scholar
Giakoumoglou, N. Pyfeats: Open-source software for image feature extraction. https://github.com/giakou4/pyfeats (2021).
Virtanen, P. et al. SciPy 1.0: Fundamental algorithms for scientific computing in Python. Nat. Methods17, 261–272. https://doi.org/10.1038/s41592-019-0686-2 (2020).
Google Scholar
Bradski, G. The OpenCV Library. Dr. Dobb’s Journal of Software Tools (2000).
Mujeeb Rahman, K. K., Nasor, M. & Imran, A. Automatic screening of diabetic retinopathy using fundus images and machine learning algorithms. Diagnostics 12, 2262. https://doi.org/10.3390/diagnostics12092262 (2022).
Google Scholar
Bibi, I., Mir, J. & Raja, G. Automated detection of diabetic retinopathy in fundus images using fused features. Phys. Eng. Sci. Med.43, 1253–1264. https://doi.org/10.1007/s13246-020-00929-5 (2020).
Google Scholar
Ali, A. et al. Machine learning based automated segmentation and hybrid feature analysis for diabetic retinopathy classification using fundus image. Entropy 22, 567. https://doi.org/10.3390/e22050567 (2020).
Google Scholar
Usman, T. M., Saheed, Y. K., Ignace, D. & Nsang, A. Diabetic retinopathy detection using principal component analysis multi-label feature extraction and classification. Int. J. Cogn. Comput. Eng.4, 78–88. https://doi.org/10.1016/j.ijcce.2023.02.002 (2023).
Google Scholar
Makmur, N. M., Kwan, F., Rana, A. D. & Kurniadi, F. I. Comparing local binary pattern and gray level co-occurrence matrix for feature extraction in diabetic retinopathy classification. Procedia Comput. Sci.227, 355–363. https://doi.org/10.1016/j.procs.2023.10.534 (2023).
Google Scholar
Żelasko, D., Ksiażek, W. & Pławiak, P. Transmission quality classification with use of fusion of neural network and genetic algorithm in pay and require multi-agent managed network. Sensors 21, 4090. https://doi.org/10.3390/s21124090 (2021).
Google Scholar
Anuradha, K., Krishna, M. V. & Mallik, B. Bio inspired boolean artificial bee colony based feature selection algorithm for sentiment classification. Meas. Sens.32, 101034. https://doi.org/10.1016/j.measen.2024.101034 (2024).
Google Scholar
Nadimi-Shahraki, M. H., Zamani, H. & Mirjalili, S. Enhanced whale optimization algorithm for medical feature selection: A COVID-19 case study. Comput. Biol. Med.148, 105858. https://doi.org/10.1016/j.compbiomed.2022.105858 (2022).
Google Scholar
Dosovitskiy, A. et al. An image is worth 16×16 words: Transformers for image recognition at scale, https://doi.org/10.48550/ARXIV.2010.11929 (2020).
Liu, Z. et al. Swin transformer: Hierarchical vision transformer using shifted windows https://doi.org/10.48550/ARXIV.2103.14030 (2021).
Liu, Z. et al. A convnet for the 2020s, https://doi.org/10.48550/ARXIV.2201.03545 (2022).
Tan, M. & Le, Q. V. Efficientnetv2: Smaller models and faster training, https://doi.org/10.48550/ARXIV.2104.00298 (2021).
