Diabetic Retinopathy Detection with Uncertainty scores: A Combined Approach Using Transfer Learning and Ensemble Calibration
Abstract The rising prevalence of diabetes has made Diabetic Retinopathy (DR) a major cause of blindness, underscoring the necessity for a computer-aided diagnostic system that can support clinical diagnoses without requiring extensive human effort. Many researchers have turned to deep learning to create automated screening and diagnostic tools for DR. However, for such systems to be truly effective in clinical practice, they must provide highly accurate assessments and well-calibrated estimates of uncertainty. Unfortunately, deep neural networks often tend to be overconfident in their predictions and are not easily amenable to probabilistic approaches. In our study, we introduce a novel approach for evaluating diagnostic uncertainty in DR predictions by employing ensemble-based calibration techniques. What sets our approach apart from cutting-edge convolutional neural network models is our use of the EfficientNet architecture, which offers superior accuracy through transfer learning. We then apply a set of post-calibration techniques to transform the model’s probabilistic output into a confidence level. To gauge the uncertainty of our forecasts, we compute the entropy of the calibrated confidence value. This approach greatly assists users in determining whether it is necessary to seek a second opinion. Our model achieves an impressive accuracy score of 96 %, and our ensemble technique exhibits a notable reduction in Expected Calibration Error (ECE), in addition to providing a reassuring uncertainty score.
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Ayhan, M. S., Kühlewein, L., Aliyeva, G., Inhoffen, W., Ziemssen, F., & Berens, P. (2020). Expert-validated estimation of diagnostic uncertainty for deep neural networks in diabetic retinopathy detection. Medical Image Analysis, 64, 101724. https://doi.org/10.1016/j.media.2020.101724
Bahramian, M., Azimzadeh Irani, A., Pourgholi, R., & Aliyari Boroujeni, A. (2024). Recognition of English Handwritten Digit using Convolutional Neural Network (CNN). Soft Computing Journal. https://doi.org/10.22052/scj.2024.243236.1021
Bai, Q., Liu, S., Tian, Y., Xu, T., Banegas‐Luna, A. J., Pérez‐Sánchez, H., ... & Yao, X. (2022). Application advances of deep learning methods for de novo drug design and molecular dynamics simulation. Wiley Interdisciplinary Reviews: Computational Molecular Science, 12(3), e1581. https://doi.org/10.1002/wcms.1581
Bijalwan, V., Semwal, V. B., & Gupta, V. (2022). Wearable sensor-based pattern mining for human activity recognition: deep learning approach. Industrial Robot: the international journal of robotics research and application, 49(1), 21-33. https://doi.org/10.1108/IR-09-2020-0187
Chen, Y., Zhu, Z., Cheng, W., Bulloch, G., Chen, Y., Liao, H., ... & Guangzhou Diabetic Eye Study group. (2023). Choriocapillaris flow deficit as a biomarker for diabetic retinopathy and diabetic macular edema: 3-year longitudinal cohort. American journal of ophthalmology, 248, 76-86. https://doi.org/10.1016/j.ajo.2022.11.018
Chollet, F. (2017). Xception: Deep learning with depthwise separable convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1251-1258).
Dai, L., Wu, L., Li, H., Cai, C., Wu, Q., Kong, H., ... & Jia, W. (2021). A deep learning system for detecting diabetic retinopathy across the disease spectrum. Nature communications, 12(1), 1-11. https://doi.org/10.1038/s41467-021-23458-5
Das, S., Kharbanda, K., Suchetha, M., Raman, R., & Dhas, E. (2021). Deep learning architecture based on segmented fundus image features for classification of diabetic retinopathy. Biomedical Signal Processing and Control, 68, 102600. https://doi.org/10.1016/j.bspc.2021.102600
Ghoshal, B., & Tucker, A. (2022). On calibrated model uncertainty in deep learning. arXiv preprint arXiv:2206.07795. https://doi.org/10.48550/arXiv.2206.07795
Guariguata, L., Whiting, D.R., Hambleton, I., et al. (2014). ‘Global estimates of diabetes prevalence for 2013 and projections for 2035’. Diabetes Research and Clinical Practice 103(2), pp. 137–149. https://doi.org/10.1016/j.diabres.2013.11.002
Guo, C., Pleiss, G., Sun, Y., & Weinberger, K. Q. (2017, July). On calibration of modern neural networks. In International conference on machine learning (pp. 1321-1330). PMLR.
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).
Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017). Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 4700-4708).
Iman, M., Rasheed, K., & Arabnia, H. R. (2022). A review of deep transfer learning and recent advancements. arXiv preprint arXiv:2201.09679. https://doi.org/10.3390/technologies11020040
Jing, Q., Yan, J., Lu, L., Xu, Y., & Yang, F. (2022). A novel method for pattern recognition of gis partial discharge via multi-information ensemble learning. Entropy, 24(7), 954. https://doi.org/10.3390/e24070954
[dataset] Kaggle APTOS 2019 Blindness Detection competition, Available: https://www.kaggle.com/c/aptos2019-blindness-detection/data. [Accessed: 2023–06–12].
Kim, H. E., Cosa-Linan, A., Santhanam, N., Jannesari, M., Maros, M. E., & Ganslandt, T. (2022). Transfer learning for medical image classification: A literature review. BMC medical imaging, 22(1), 69. https://doi.org/10.1186/s12880-022-00793-7
Kobat, S. G., Baygin, N., Yusufoglu, E., Baygin, M., Barua, P. D., Dogan, S., ... & Acharya, U. R. (2022). Automated diabetic retinopathy detection using horizontal and vertical patch division-based pre-trained DenseNET with digital fundus images. Diagnostics, 12(8), 1975. https://doi.org/10.3390/diagnostics12081975
Kwee, A., Teo, Z. L., & Ting, D. S. W. (2022). Digital health in medicine: Important considerations in evaluating health economic analysis. The Lancet Regional Health–Western Pacific, 23. https://doi.org/10.1016/j.lanwpc.2022.100476
Martinez-Murcia, F. J., Ortiz, A., Ramírez, J., Górriz, J. M., & Cruz, R. (2021). Deep residual transfer learning for automatic diagnosis and grading of diabetic retinopathy. Neurocomputing, 452, 424-434. https://doi.org/10.1016/j.neucom.2020.04.148
Mazraeh, H. D., & Parand, K. (2024). GEPINN: An innovative hybrid method for a symbolic solution to the Lane–Emden type equation based on grammatical evolution and physics-informed neural networks. Astronomy and Computing, 48, 100846. https://doi.org/10.1016/j.ascom.2024.100846
Mazraeh, H. D., & Parand, K. (2025a). A three-stage framework combining neural networks and Monte Carlo tree search for approximating analytical solutions to the Thomas–Fermi equation. Journal of Computational Science, 87, 102582. https://doi.org/10.1016/j.jocs.2025.102582
Mazraeh, H. D., & Parand, K. (2025b). An innovative combination of deep Q-networks and context-free grammars for symbolic solutions to differential equations. Engineering Applications of Artificial Intelligence, 142, 109733. https://doi.org/10.1016/j.engappai.2024.109733
Mazraeh, H. D & Parand, K. (2025c). Approximate symbolic solutions to differential equations using a novel combination of Monte Carlo tree search and physics-informed neural networks approach. Engineering with Computers, 1-29. https://doi.org/10.1007/s00366-025-02127-x
Mirzania, D., Thompson, A. C., & Muir, K. W. (2021). Applications of deep learning in detection of glaucoma: a systematic review. European Journal of Ophthalmology, 31(4), 1618-1642. https://doi.org/10.1177/1120672120977346
Mukhamadiyev, A., Khujayarov, I., Djuraev, O., & Cho, J. (2022). Automatic speech recognition method based on deep learning approaches for Uzbek language. Sensors, 22(10), 3683. https://doi.org/10.3390/s22103683
Oulhadj, M., Riffi, J., Chaimae, K., Mahraz, A. M., Ahmed, B., Yahyaouy, A., ... & Tairi, H. (2022). Diabetic retinopathy prediction based on deep learning and deformable registration. Multimedia Tools and Applications, 81(20), 28709-28727. https://doi.org/10.1007/s11042-022-12968-z
Pan, C. W., Cheung, C. Y., Aung, T., Cheung, C. M., Zheng, Y. F., Wu, R. Y., ... & Saw, S. M. (2013). Differential associations of myopia with major age-related eye diseases: the Singapore Indian Eye Study. Ophthalmology, 120(2), 284-291.
Pandey, M., Fernandez, M., Gentile, F., Isayev, O., Tropsha, A., Stern, A. C., & Cherkasov, A. (2022). The transformational role of GPU computing and deep learning in drug discovery. Nature Machine Intelligence, 4(3), 211-221. https://doi.org/10.1038/s42256-022-00463-x https://doi.org/10.1016/j.ophtha.2012.07.065
Pao, S. I., Lin, H. Z., Chien, K. H., Tai, M. C., Chen, J. T., & Lin, G. M. (2020). Detection of diabetic retinopathy using bichannel convolutional neural network. Journal of Ophthalmology. https://doi.org/10.1155/2020/9139713
Platt, J. (1999). Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. Advances in large margin classifiers, 10(3), 61-74.
Prasath, A. (2022). Design of an integrated learning approach to assist real-time deaf application using voice recognition system. Computers and Electrical Engineering, 102, 108145. https://doi.org/10.1016/j.compeleceng.2022.108145
Roßbach, J., Kollmeier, B., & Meyer, B. T. (2022). A model of speech recognition for hearing-impaired listeners based on deep learning. The Journal of the Acoustical Society of America, 151(3), 1417-1427. https://doi.org/10.1121/10.0009411
Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556. https://doi.org/10.48550/arXiv.1409.1556
Suganyadevi, S., Seethalakshmi, V., & Balasamy, K. (2022). A review on deep learning in medical image analysis. International Journal of Multimedia Information Retrieval, 11(1), 19-38. https://doi.org/10.1007/s13735-021-00218-1
Sugeno, A., Ishikawa, Y., Ohshima, T., & Muramatsu, R. (2021). Simple methods for the lesion detection and severity grading of diabetic retinopathy by image processing and transfer learning. Computers in Biology and Medicine, 137, 104795. https://doi.org/10.1016/j.compbiomed.2021.104795
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).
Tan, C., Sun, F., Kong, T., Zhang, W., Yang, C., & Liu, C. (2018). A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7. 2018, Proceedings, Part III 27 (pp. 270-279). Springer International Publishing. https://doi.org/10.1007/978-3-030-01424-7_27
Tan, M., & Le, Q. (2019). EfficientNet, Rethinking model scaling for convolutional neural networks. In: International Conference on Machine Learning, PMLR, pp. 6105–6114.
Tian, J., Smith, G., Guo, H., Liu, B., Pan, Z., Wang, Z., & Fang, R. (2021). Modular machine learning for Alzheimer’s disease classification from retinal vasculature. Scientific Reports, 11(1), 1-11. https://doi.org/10.1038/s41598-020-80312-2
Tsuneki, M. (2022). Deep learning models in medical image analysis. Journal of Oral Biosciences. https://doi.org/10.1016/j.job.2022.03.003
Uhm, K. H., Jung, S. W., Choi, M. H., Shin, H. K., Yoo, J. I., Oh, S. W., ... & Ko, S. J. (2021). Deep learning for end-to-end kidney cancer diagnosis on multi-phase abdominal computed tomography. NPJ precision oncology, 5(1), 54. https://doi.org/10.1038/s41698-021-00195-y
Van der Velden, B. H., Kuijf, H. J., Gilhuijs, K. G., & Viergever, M. A. (2022). Explainable artificial intelligence (XAI) in deep learning-based medical image analysis. Medical Image Analysis, 102470. https://doi.org/10.1016/j.media.2022.102470
Wen, L. H., & Jo, K. H. (2022). Deep learning-based perception systems for autonomous driving: A comprehensive survey. Neurocomputing. https://doi.org/10.1016/j.neucom.2021.08.155
Ye, T., Si, S., Wang, J., Cheng, N., & Xiao, J. (2022). Uncertainty Calibration for Deep Audio Classifiers. arXiv preprint arXiv:2206.13071. https://doi.org/10.48550/arXiv.2206.13071
Zadrozny, B., & Elkan, C. (2001, June). Obtaining calibrated probability estimates from decision trees and naive bayesian classifiers. In Icml, 1, pp. 609-616.
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