Skip to main content
Springer Nature Link
Log in

Accurate and Explainable Retinal Disease Recognition via DCNFIS

  • Conference paper
  • First Online:
Fuzzy Information Processing 2023 (NAFIPS 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 751))

Included in the following conference series:

  • 238 Accesses

Abstract

The accuracy-interpretability tradeoff is a significant challenge for Explainable AI systems; if too much accuracy is lost, an explainable system might be of no actual value. We report on the ongoing development of the Deep Convolutional Neuro-Fuzzy Inference System, an XAI algorithm that has to this point demonstrated accuracy on par with existing convolutional neural networks. Our system is evaluated on the Retinal OCT dataset, in which it achieves state-of-the-art performance. Explanations for the system’s classifications based on saliency analysis of medoid elements from the fuzzy rules in the classifier component are analyzed.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    A layer of neuronal synapses in the retina of the eye [6].

  2. 2.

    A small depression within the neurosensory retina where visual acuity is the highest [6].

  3. 3.

    A thin layer of tissue that is part of the middle layer of the wall of the eye, between the sclera (white outer layer of the eye) and the retina [6].

References

  1. Abayomi-Alli, O.O., Damasevicius, R., Misra, S., Maskeliunas, R., Abayomi-Alli, A.: Malignant skin melanoma detection using image augmentation by oversamplingin nonlinear lower-dimensional embedding manifold. Turk. J. Electr. Eng. Comput. Sci. 29(8), 2600–2614 (2021)

    Article  Google Scholar 

  2. Akkus, Z., Galimzianova, A., Hoogi, A., Rubin, D.L., Erickson, B.J.: Deep learning for brain MRI segmentation: state of the art and future directions (2017). https://doi.org/10.1007/s10278-017-9983-4

  3. Amoroso, N., et al.: A roadmap towards breast cancer therapies supported by explainable artificial intelligence. Appl. Sci. 11(11), 4881 (2021)

    Article  Google Scholar 

  4. Ancona, M., Ceolini, E., Öztireli, C., Gross, M.: Towards better understanding of gradient-based attribution methods for deep neural networks. arXiv preprint arXiv:1711.06104 (2017)

  5. Aviles, A.I., Alsaleh, S.M., Montseny, E., Sobrevilla, P., Casals, A.: A deep-neuro-fuzzy approach for estimating the interaction forces in robotic surgery (2016). https://doi.org/10.1109/FUZZ-IEEE.2016.7737812

  6. Binder, S.: The Macula: Diagnosis, Treatment and Future Trends. Springer Science & Business Media, Vienna (2004). https://doi.org/10.1007/978-3-7091-7985-7

  7. Chahardoli, R., Barbosa, D., Rafiei, D.: Relation extraction with synthetic explanations and neural network. In: 2021 International Symposium on Electrical, Electronics and Information Engineering, pp. 254–262 (2021)

    Google Scholar 

  8. Dhungel, N., Carneiro, G., Bradley, A.P.: The automated learning of deep features for breast mass classification from mammograms. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 106–114. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46723-8_13

    Chapter  Google Scholar 

  9. Dindorf, C., et al.: Classification and automated interpretation of spinal posture data using a pathology-independent classifier and explainable artificial intelligence (XAI). Sensors 21 (2021). https://doi.org/10.3390/s21186323

  10. Dong, Y., Su, H., Zhu, J., Zhang, B.: Improving interpretability of deep neural networks with semantic information, vol. 2017-January (2017). https://doi.org/10.1109/CVPR.2017.110

  11. Eigner, I., Bodendorf, F., Wickramasinghe, N.: Predicting high-cost patients by machine learning: a case study in an Australian private hospital group (2019). https://doi.org/10.29007/jw6h

  12. Feng, Q., Chen, L., Chen, C.L.P., Guo, L.: Deep fuzzy clustering-a representation learning approach. IEEE Trans. Fuzzy Syst. 28 (2020). https://doi.org/10.1109/TFUZZ.2020.2966173

  13. Gu, D., et al.: Vinet: a visually interpretable image diagnosis network. IEEE Trans. Multimed. 22 (2020). https://doi.org/10.1109/TMM.2020.2971170

  14. Guan, C., Wang, S., Liew, A.W.C.: Lip image segmentation based on a fuzzy convolutional neural network. IEEE Trans. Fuzzy Syst. 28 (2020). https://doi.org/10.1109/TFUZZ.2019.2957708

  15. Gunning, D., Vorm, E., Wang, Y., Turek, M.: Darpa’s explainable AI (XAI) program: a retrospective. Authorea Preprints (2021)

    Google Scholar 

  16. Haykin, S.: Neural Networks and Learning Machines, vol. 3 (2008). 978–0131471399

    Google Scholar 

  17. He, J., Wang, J., Han, Z., Ma, J., Wang, C., Qi, M.: An interpretable transformer network for the retinal disease classification using optical coherence tomography. Sci. Rep. 13(1), 3637 (2023)

    Article  Google Scholar 

  18. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90

  19. He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9908, pp. 630–645. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46493-0_38

    Chapter  Google Scholar 

  20. Jafari, R., Razvarz, S., Gegov, A.: Neural network approach to solving fuzzy nonlinear equations using z-numbers. IEEE Trans. Fuzzy Syst. 28 (2020). https://doi.org/10.1109/TFUZZ.2019.2940919

  21. Jang, J., Sun, C., Mizutani, E.: Neuro-fuzzy and soft computing-a computational approach to learning and machine intelligence [book review]. IEEE Trans. Autom. Control 42 (2005). https://doi.org/10.1109/tac.1997.633847

  22. Jang, J.S.R.: ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans. Syst. Man Cybern. 23 (1993). https://doi.org/10.1109/21.256541

  23. John, V., Mita, S., Liu, Z., Qi, B.: Pedestrian detection in thermal images using adaptive fuzzy c-means clustering and convolutional neural networks (2015). https://doi.org/10.1109/MVA.2015.7153177

  24. Kermany, D.S., et al.: Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell 172, 1122–1131.e9 (2018). https://doi.org/10.1016/J.CELL.2018.02.010

  25. Kermany, D.S., et al.: Large dataset of labeled optical coherence tomography (oct) and chest x-ray images. Mendeley Data 2 (2018)

    Google Scholar 

  26. Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)

  27. Kourou, K., Exarchos, T.P., Exarchos, K.P., Karamouzis, M.V., Fotiadis, D.I.: Machine learning applications in cancer prognosis and prediction. Comput. Struct. Biotechnol. J.13, 8–17 (2015). https://doi.org/10.1016/J.CSBJ.2014.11.005

  28. Liu, Z., et al.: Swin transformer: hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 10012–10022 (2021)

    Google Scholar 

  29. Miere, A., et al.: Deep learning-based classification of inherited retinal diseases using fundus autofluorescence. J. Clin. Med. 9 (2020). https://doi.org/10.3390/jcm9103303

  30. Nilsson, N.J.: The Quest for Artificial Intelligence. Cambridge University Press, Cambridge (2009). https://doi.org/10.1017/CBO9780511819346

  31. Ovchinnikov, S.: Similarity relations, fuzzy partitions, and fuzzy orderings. Fuzzy Sets Syst. 40 (1991). https://doi.org/10.1016/0165-0114(91)90048-U

  32. Pekala, M., Joshi, N., Liu, T.Y., Bressler, N.M., DeBuc, D.C., Burlina, P.: Deep learning based retinal oct segmentation. Comput. Biol. Med. 114, 103445 (2019). https://doi.org/10.1016/J.COMPBIOMED.2019.103445

  33. Plis, S.M., et al.: Deep learning for neuroimaging: a validation study. Front. Neurosci. (2014). https://doi.org/10.3389/fnins.2014.00229

    Article  Google Scholar 

  34. Pratt, H., Coenen, F., Broadbent, D.M., Harding, S.P., Zheng, Y.: Convolutional neural networks for diabetic retinopathy, vol. 90 (2016). https://doi.org/10.1016/j.procs.2016.07.014

  35. Rudin, C.: Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead (2019). https://doi.org/10.1038/s42256-019-0048-x

  36. Samhan, B., Crampton, T., Ruane, R.: The trajectory of it in healthcare at HICSS: a literature review, analysis, and future directions. Commun. Assoc. Inf. Syst. 43 (2018). https://doi.org/10.17705/1CAIS.04341

  37. Sarabakha, A., Kayacan, E.: Online deep fuzzy learning for control of nonlinear systems using expert knowledge. IEEE Trans. Fuzzy Syst. 28(7), 1492–1503 (2019)

    Google Scholar 

  38. Schiffman, J.S., Patel, N.B., Cruz, R.A., Tang, R.A.: Optical coherence tomography for the radiologist. Neuroimaging Clin. 25(3), 367–382 (2015)

    Article  Google Scholar 

  39. Simonyan, K., Vedaldi, A., Zisserman, A.: Deep inside convolutional networks: visualising image classification models and saliency maps. arXiv preprint arXiv:1312.6034 (2013)

  40. Soleymani, A., Asl, A.A.S., Yeganejou, M., Dick, S., Tavakoli, M., Li, X.: Surgical skill evaluation from robot-assisted surgery recordings. In: 2021 International Symposium on Medical Robotics (ISMR), pp. 1–6. IEEE (2021)

    Google Scholar 

  41. Soomro, T., Talks, J.: The use of optical coherence tomography angiography for detecting choroidal neovascularization, compared to standard multimodal imaging. Eye 32(4), 661–672 (2018)

    Article  Google Scholar 

  42. Struyf, A., Hubert, M., Rousseeuw, P.: Clustering in an object-oriented environment. J. Stat. Softw. 1, 1–30 (1997)

    Google Scholar 

  43. Sun, C.T., Jang, J.S.: A neuro-fuzzy classifier and its applications. In: Proceedings of the [Proceedings 1993] Second IEEE International Conference on Fuzzy Systems, pp. 94–98. IEEE (1993)

    Google Scholar 

  44. Szegedy, C., et al.: Going deeper with convolutions, vol. 07-12-June-2015 (2015). https://doi.org/10.1109/CVPR.2015.7298594

  45. Wang, D., Wang, L.: On oct image classification via deep learning. IEEE Photonics J. 11(5), 1–14 (2019)

    MathSciNet  Google Scholar 

  46. Wang, H., Ji, Y., Song, K., Sun, M., Lv, P., Zhang, T.: VIT-P: classification of genitourinary syndrome of menopause from oct images based on vision transformer models. IEEE Trans. Instrum. Meas. 70, 1–14 (2021). https://doi.org/10.1109/TIM.2021.3122121

    Article  Google Scholar 

  47. Wen, H., et al.: Towards more efficient ophthalmic disease classification and lesion location via convolution transformer. Comput. Methods Programs Biomed. 220, 106832 (2022)

    Article  Google Scholar 

  48. Yang, J., et al.: Medmnist v2 - a large-scale lightweight benchmark for 2D and 3D biomedical image classification. Sci. Data 10, 41 (2023)

    Google Scholar 

  49. Yeganejou, M.: Interpretable deep convolutional fuzzy networks (2019)

    Google Scholar 

  50. Yeganejou, M., Dick, S.: Classification via deep fuzzy c-means clustering, vol. 2018-July (2018). https://doi.org/10.1109/FUZZ-IEEE.2018.8491461

  51. Yeganejou, M., Dick, S.: Improved deep fuzzy clustering for accurate and interpretable classifiers, vol. 2019-June (2019). https://doi.org/10.1109/FUZZ-IEEE.2019.8858809

  52. Yeganejou, M., Dick, S., Miller, J.: Interpretable deep convolutional fuzzy classifier. IEEE Trans. Fuzzy Syst. 28(7), 1407–1419 (2019)

    Google Scholar 

  53. Yeganejou, M., Kluzinski, R., Dick, S., Miller, J.: An end-to-end trainable deep convolutional neuro-fuzzy classifier. In: 2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1–7. IEEE (2022)

    Google Scholar 

  54. Zhang, Z., Huang, M., Liu, S., Xiao, B., Durrani, T.S.: Fuzzy multilayer clustering and fuzzy label regularization for unsupervised person reidentification. IEEE Trans. Fuzzy Syst. 28 (2020). https://doi.org/10.1109/TFUZZ.2019.2914626

  55. Zheng, Y.J., Chen, S.Y., Xue, Y., Xue, J.Y.: A pythagorean-type fuzzy deep denoising autoencoder for industrial accident early warning. IEEE Trans. Fuzzy Syst. 25 (2017). https://doi.org/10.1109/TFUZZ.2017.2738605

  56. Zwaan, L., Singh, H.: The challenges in defining and measuring diagnostic error. Diagnosis 2 (2015). https://doi.org/10.1515/dx-2014-0069

Download references

Author information

Authors and Affiliations

  1. University of Alberta, Edmonton, Canada

    Mojtaba Yeganejou, Mohammad Keshmiri & Scott Dick

Authors
  1. Mojtaba Yeganejou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohammad Keshmiri
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Scott Dick
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Scott Dick .

Editor information

Editors and Affiliations

  1. University of Cincinnati, Cincinnati, OH, USA

    Kelly Cohen

  2. Thales Avionics, Cincinnati, OH, USA

    Nicholas Ernest

  3. DigiPen Institute of Technology, Redmond, WA, USA

    Barnabas Bede

  4. Department of Computer Science, University of Texas at El Paso, El Paso, TX, USA

    Vladik Kreinovich

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Yeganejou, M., Keshmiri, M., Dick, S. (2023). Accurate and Explainable Retinal Disease Recognition via DCNFIS. In: Cohen, K., Ernest, N., Bede, B., Kreinovich, V. (eds) Fuzzy Information Processing 2023. NAFIPS 2023. Lecture Notes in Networks and Systems, vol 751. Springer, Cham. https://doi.org/10.1007/978-3-031-46778-3_1

Download citation

Publish with us

Policies and ethics