Decision-layer interpretability for CNN-based glaucoma classification via sparse feature selection and ANFIS
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Keywords:
adaptive neuro-fuzzy inference system (ANFIS), classification, convolutional neural network (CNN), explainable AI, glaucoma
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Abstract
Glaucoma is a leading cause of irreversible vision loss, and its early detection remains challenging due to the presence of subtle structural varitions in retinal fundus images. Convolutional neural networks (CNNs) have demonstrated strong performance for automated classification of glaucoma; however, the relationship between extracted features and prediction outcomes frequently proves challenging to interpret. Most existing explainable artificial intelligence (XAI) approaches rely on post hoc visualizations, which provide limited insight into the decisions-making process. To address this limitation, this present study proposes a hybrid CNN–feature selection–ANFIS framework (CNN–FS–ANFIS) that integrates interpretability directly within the decision layer. In this framework, the first stage of the process involves adapting a CNN backbone to the glaucoma classification task through the use of transfer learning. This is then used as a fixed feature extractor to obtain retinal representations for decision-layer modeling. Subsequently, a feature selection stage is applied driven by sparsity to construct a compact and structured subset of informative features. These features are then fed into an Adaptive Neuro-Fuzzy Inference System (ANFIS), enabling predictions to be expressed through explicit fuzzy rule-based reasoning. The impact of feature compactness is examined in a controlled experimental setting, where the feature subset size is varied from three to nine. The findings demonstrate that compact feature subsets can achieve consistent and competitive performance. By means of LASSO-selected features, the ANFIS decision layer achieved an AUC of 0.84±0.01, sensitivity of 0.82±0.13, specificity of 0.74±0.10, and an F1-score of 0.79±0.04. Rule-base analysis further exhibited that two-to three-rule ANFIS configurations-maintained AUC values of approximately 0.84 while preserving a transparent and manageable decision structure. The proposed framework, therefore, enables direct analysis of the relationship between selected CNN features, fuzzy rules, and model outputs. This traceable decision pathway has the potential to support more transparent and auditable glaucoma screening systems.
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Irijanti, E., Ardiyanto, I., & Nugroho, H. A. (2026). Decision-layer interpretability for CNN-based glaucoma classification via sparse feature selection and ANFIS. Communications in Science and Technology, 11(1), 281–301. https://doi.org/10.21924/cst.11.1.2026.1968
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References
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2. X. Li, G.-X. Huang, R. Li, Z.-J. Zhang, S.-Q. Zhang, L. Zhu, et al. 2021 Global Burden of Disease Study: a comprehensive analysis of glaucoma in the middle-aged and older adult population at global, regional, and national levels. Front. Public Health 13 (2025) 1526061.
3. S. Shan, J. Wu, J. Cao, Y. Feng, J. Zhou, Z. Luo, et al. Global incidence and risk factors for glaucoma: a systematic review and meta-analysis of prospective studies. J. Glob. Health 14 (2024) 04252.
4. Z. Wang, C. C. Xue, Y. Li, et al. Global glaucoma prevalence: burden and projection to 2060. Am. J. Ophthalmol. 283 (2026) 324–335.
5. W. M. Liao, B. J. Zou, R. C. Zhao, Y. Q. Chen, Z. Y. He, M. J. Zhou. Clinical interpretable deep learning model for glaucoma diagnosis. IEEE J. Biomed. Health Inform. 24 (2020) 1405–1412.
6. X. Yang, J. Wu, X. Wang, Y. Yuan, J. Li, G. Chen, et al. Multi-scale spatio-temporal transformer-based imbalanced longitudinal learning for glaucoma forecasting from irregular time series images. IEEE J. Biomed. Health Inform. 29 (2025) 2859–2870.
7. R. Hemelings, B. Elen, A. K. Schuster, M. B. Blaschko, J. Barbosa-Breda, P. Hujanen, et al. A generalizable deep learning regression model for automated glaucoma screening from fundus images. NPJ Digit. Med. 6 (2023) 112.
8. S. A. Haja, V. Mahadevappa. Advancing glaucoma detection with convolutional neural networks: a paradigm shift in ophthalmology. Rom. J. Ophthalmol. 67 (2023) 222–237.
9. S. Sangchocanonta, P. Pooprasert, N. Lerthirunvibul, K. Patchimnan, P. Phienphanich, A. Munthuli, et al. Optimizing deep learning models for glaucoma screening with vision transformers for resource efficiency and the pie augmentation method. PLOS ONE 20 (2025) e0314111.
10. H. A. Nugroho, T. Kirana, V. Pranowo, A. H. T. Hutami. Optic cup segmentation using adaptive threshold and morphological image processing. Commun. Sci. Technol. 4 (2019) 63–67.
11. S. Saha, J. Vignarajan, S. Frost. A fast and fully automated system for glaucoma detection using color fundus photographs. Scientific Reports 13 (2023) 44473.
12. R. Arnay, J. Hernández-Aceituno, T. Dı́az-Alemán, J. M. Martı́nez-de-la Casa, J. Sigut. Optic cup segmentation of stereo retinal fundus images using virtual reality. Med. Biol. Eng. Comput. 61 (2023) 1421–1434.
13. Y. Xu, M. Hu, H. Liu, H. Yang, H. Wang, S. Lu, et al. A hierarchical deep learning approach with transparency and interpretability based on small samples for glaucoma diagnosis. NPJ Digit. Med. 4 (2021).
14. X. Huang, M. R. Islam, S. Akter, F. Ahmed, E. Kazami, H. A. Serhan, et al. Artificial intelligence in glaucoma: opportunities, challenges, and future directions. Biomed. Eng. Online 22 (2023) 126.
15. J.-A. Kim, H. Yoon, D. Lee, M. Kim, J. Choi, E. J. Lee, et al. Development of a deep learning system to detect glaucoma using macular vertical optical coherence tomography scans of myopic eyes. Scientific Reports 13 (2023) 13642.
16. Y. Hagiwara, O.-A. Ciora, M. Monnet, G. Lancho, J. M. Lorenz. AI-driven approaches for glaucoma detection – a comprehensive review. Diagnostics 14 (2024) 52.
17. D. M. H. Nguyen, H. M. T. Alam, T. Nguyen, D. Srivastav, H.-J. Profitlich, N. Le, et al. Deep learning for ophthalmology: the state-of-the-art and future trends, 2025. arXiv preprint.
18. V. K. Velpula, L. D. Sharma. Automatic glaucoma detection from fundus images using deep convolutional neural networks and exploring networks behaviour using visualization techniques. SN Computer Science 4 (2023).
19. O. Kovalyk, J. Morales-Sánchez, R. Verdú-Monedero, I. Sellés-Navarro, A. Palazón-Cabanes, J.-L. Sancho-Gómez. PAPILA: Dataset with fundus images and clinical data of both eyes of the same patient for glaucoma assessment. Scientific Data 9 (2022) 291.
20. A. Sánchez-Morales, J. Morales-Sánchez, O. Kovalyk, R. Verdú-Monedero, J.-L. Sancho-Gómez. Improving glaucoma diagnosis assembling deep networks and voting schemes. Diagnostics 12 (2022) 1382.
21. D. J. Nugraha, N. Yudistira, A. W. Widodo. Weighted loss for imbalanced glaucoma detection: insights from visual explanations. Comput. Biol. Med. 196 (2025) 110875.
22. X. Kui, Z. Hai, B. Zou, Y. Li, W. Liang, Z. Ming, et al. PK-Net: a prior knowledge-driven dual-path network for enhanced glaucoma screening. Knowl.-Based Syst. 329 (2025) 114374.
23. J. Sigut, F. Fumero, J. Estévez, S. Alayón, T. Dı́az-Alemán. In-depth evaluation of saliency maps for interpreting convolutional neural network decisions in the diagnosis of glaucoma based on fundus imaging. Sensors 24 (2024) 239.
24. M. D. Abramoff, M. K. Garvin, M. Sonka. Retinal imaging and image analysis. IEEE Rev. Biomed. Eng. 3 (2010) 169–208.
25. D. N. K. Hardani, I. Ardiyanto, H. Adi Nugroho. Decoding brain tumor insights: Evaluating CAM variants with 3D U-Net for segmentation. Commun. Sci. Technol. 9 (2024) 262–273.
26. G. Chandrashekar, F. Sahin. A survey on feature selection methods. Comput. Electr. Eng. 40 (2014) 16–28.
27. B. Singh, M. Doborjeh, Z. Doborjeh, S. Budhraja, S. Tan, A. Sumich, et al. Constrained Neuro-Fuzzy Inference Methodology for Explainable Personalised Modelling with Applications on Gene Expression Data. Scientific Reports 13 (2023) 456.
28. Y. A. Sagar, M. S. R. L. Reddy, S. Shilpa, N. Jyothi, A. Velivela, A. N. Rao. Neuro-fuzzy systems: neural networks and fuzzy logic integration in soft computing. In Cybernetics, Human Cognition, and Machine Learning in Communicative Applications, pages 39–49. Springer Nature Singapore, 2025.
29. J.-S. R. Jang. ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans. Syst. Man Cybern. 23 (1993) 665–685.
30. M. Yeganejou, S. Dick, J. Miller. Interpretable deep convolutional fuzzy classifier. IEEE Trans. Fuzzy Syst. 28 (2020) 1407–1419.
31. H. Fu, J. Cheng, Y. Xu, C. Zhang, D. W. K. Wong, J. Liu, et al. Disc-aware ensemble network for glaucoma screening from fundus image. IEEE Trans. Med. Imaging 37 (2018) 2493–2501.
32. E. Irijanti, H. A. Nugroho, I. Ardiyanto. Performance analysis for glaucoma diagnosis using transfer learning on the PAPILA dataset. In 2023 International Workshop on Artificial Intelligence and Image Processing (IWAIIP), pages 213–218, 2023.
33. F. Calimeri, A. Marzullo, C. Stamile, G. Terracina. Optic Disc Detection Using Fine Tuned Convolutional Neural Networks. In 2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), pages 69–75, 2016.
34. H. Fu, J. Cheng, Y. Xu, D. W. K. Wong, J. Liu, X. Cao. Joint Optic Disc and Cup Segmentation Based on Multi-label Deep Network and Polar Transformation. IEEE Trans. Med. Imaging 37 (2018) 1597–1605.
35. M. Tan, Q. V. Le. EfficientNetV2: Smaller Models and Faster Training. In Proceedings of the 38th International Conference on Machine Learning, volume 139 of Proceedings of Machine Learning Research, pages 10096–10106. PMLR, 2021.
36. E. Irijanti, H. A. Nugroho, I. Ardiyanto. EfficientNet model for detecting glaucoma on the PAPILA dataset. In 2024 7th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI), pages 62–67, 2024.
37. Y. Bengio, A. Courville, P. Vincent. Representation learning: a review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35 (2013) 1798–1828.
38. B. O. Ayinde, T. Inanc, J. M. Zurada. On correlation of features extracted by deep neural networks. IEEE Trans. Neural Netw. Learn. Syst. 30 (2019) 3138–3150.
39. I. Guyon, A. Elisseeff. An Introduction to Variable and Feature Selection. J. Mach. Learn. Res. 3 (2003) 1157–1182.
40. A. D. Baddeley. Working memory. Science 255 (1992) 556–559.
41. N. Cowan. The magical number 4 in short-term memory: a reconsideration of mental storage capacity. Behav. Brain Sci. 24 (2001) 87–114.
42. G. A. Miller. The magical number seven, plus or minus two: some limits on our capacity for processing information. Psychological Review 63 (1956) 81–97.
43. R. Tibshirani. Regression shrinkage and selection via the lasso. J. R. Stat. Soc. Ser. B Stat. Methodol. 58 (1996) 267–288.
44. H. Zou, T. Hastie. Regularization and variable selection via the elastic net. J. R. Stat. Soc. Ser. B Stat. Methodol. 67 (2005) 301–320.
45. T. M. Cover, J. A. Thomas. Elements of Information Theory. Wiley-Interscience, Hoboken, NJ, 2 edition, 2006.
46. J. Demšar. Statistical Comparisons of Classifiers over Multiple Data Sets. J. Mach. Learn. Res. 7 (2006) 1–30.
47. R. Caruana, A. Niculescu-Mizil. An empirical comparison of supervised learning algorithms. In Proceedings of the 23rd International Conference on Machine Learning (ICML), pages 161–168. ACM, 2006.
48. J. R. Quinlan. C4.5: Programs for Machine Learning. Morgan Kaufmann, San Mateo, CA, 1993.
49. W. W. Cohen. Fast effective rule induction. In Proceedings of the 12th International Conference on Machine Learning (ICML), pages 115–123. Morgan Kaufmann, 1995.
50. C. Cortes, V. Vapnik. Support-vector networks. Machine Learning 20 (1995) 273–297.
51. L. Breiman. Random forests. Machine Learning 45 (2001) 5–32.
52. T. Hastie, R. Tibshirani, J. Friedman. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer, 2 edition, 2009.
53. F. Fityah, N. A. Setiawan, D. W. Anggrahini. Interpretability evaluation of rule-based classifier in myocardial infarction classification based on syntactical features of ECG signal. Commun. Sci. Technol. 10 (2025) 460–466.
54. D. P. Kingma, J. Ba. Adam: A Method for Stochastic Optimization. In International Conference on Learning Representations, 2015.
55. I. Loshchilov, F. Hutter. Decoupled Weight Decay Regularization. In International Conference on Learning Representations, 2019.
56. T. Dozat. Incorporating Nesterov Momentum into Adam. In Proceedings of the 4th International Conference on Learning Representations, 2016.
57. L. Liu, H. Jiang, P. He, W. Chen, X. Liu, J. Gao, et al. On the Variance of the Adaptive Learning Rate and Beyond. In International Conference on Learning Representations, 2020.
58. A. Blumer, A. Ehrenfeucht, D. Haussler, M. K. Warmuth. Occam’s razor. Inf. Process. Lett. 24 (1987) 377–380.
59. Y. Wang, A. Paschke. Extracting interpretable hierarchical rules from deep neural networks’ latent space. In Rules and Reasoning, pages 238–253. Springer Nature Switzerland, 2023.
60. D. Zhang, T. Chen. Scikit-ANFIS: a scikit-learn compatible Python implementation for adaptive neuro-fuzzy inference system. Int. J. Fuzzy Syst. 26 (2024) 2039–2057.
61. A. Diaz-Pinto, S. Morales, V. Naranjo, T. Köhler, J. M. Mossi, A. Navea. CNNs for Automatic Glaucoma Assessment Using Fundus Images: An Extensive Validation. Biomed. Eng. Online 18 (2019) 29.
62. J. I. Orlando, H. Fu, J. B. Breda, K. van Keer, D. R. Bathula, A. Diaz-Pinto, et al. REFUGE Challenge: A Unified Framework for Evaluating Automated Methods for Glaucoma Assessment from Fundus Photographs. Med. Image Anal. 59 (2020) 101570.
63. Z. Zhang, F. S. Yin, J. Liu, D. W. K. Wong, N. M. Tan, B. H. Lee, et al. ORIGA(-light): An Online Retinal Fundus Image Database for Glaucoma Analysis and Research. In 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pages 3065–3068, 2010.