Abstract
Infectious keratitis is the leading cause of blindness in the word where bacteria keratitis (BK) and fungi keratitis (FK) are common causes of infection. As an ophthalmic emergency, BK and FK need to be treated correctly as soon as possible to prevent irrecoverable damage to vision, but the early correct diagnosis between them is challenging. Some research have shown that even trained ophthalmologists have less than 80% accuracy in correctly diagnosing FK from BK. In this paper, a Fine-Grained model called BF-Net is proposed to improve the accuracy of automatic diagnosis of keratitis, with consideration of the characteristics of keratitis images. We build a keratitis dataset containing 1433 Slit-Lamp images of BK or FK from 458 patients and conducted detailed experiments to prove the effectiveness of our method. The Precision, Recall, Accuracy, AUC and F1 score of our method are 82.34%, 87.20%, 83.12%, 0.85 and 0.85 respectively, which has achieved the best effect compared with other classification methods. Furthermore, visualization technique Grad-CAM++ is used to provide interpretability for the validity of our model.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Cabrera-Aguas, M., Khoo, P., Watson, S.L.: Infectious keratitis: a review. Clin. Exp. Ophthalmol. 50(5), 543–562 (2022)
Durand, M.L., Barshak, M.B., Chodosh, J.: Infectious keratitis in 2021. JAMA 326(13), 1319–1320 (2021)
Ibrahim, Y.W., Boase, D.L., Cree, I.A.: Epidemiological characteristics, predisposing factors and microbiological profiles of infectious corneal ulcers: the portsmouth corneal ulcer study. Br. J. Ophthalmol. 93(10), 1319–1324 (2009)
Dalmon, C., et al.: The clinical differentiation of bacterial and fungal keratitis: a photographic survey. Invest. Ophthalmol. Vis. Sci. 53(4), 1787–1791 (2012)
Hung, N., et al.: Using slit-lamp images for deep learning-based identification of bacterial and fungal keratitis: model development and validation with different convolutional neural networks. Diagnostics 11(7), 1246 (2021)
Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017)
Xu, Y., et al.: Deep sequential feature learning in clinical image classification of infectious keratitis. Engineering 7(7), 1002–1010 (2021)
Mayya, V., Kamath Shevgoor, S., Kulkarni, U., Hazarika, M., Barua, P.D., Acharya, U.R.: Multi-scale convolutional neural network for accurate corneal segmentation in early detection of fungal keratitis. J. Fungi 7(10), 850 (2021)
Zhong, Z., Zheng, L., Kang, G., Li, S., Yang, Y.: Random erasing data augmentation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 13001–13008 (2020)
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255. IEEE (2009)
Sun, C., Shrivastava, A., Singh, S., Gupta, A.: Revisiting unreasonable effectiveness of data in deep learning era. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 843–852 (2017)
Ying, X.: An overview of overfitting and its solutions. In: Journal of Physics: Conference Series, vol. 1168, p. 022022. IOP Publishing (2019)
Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132–7141 (2018)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Zhuang, P., Wang, Y., Qiao, Y.: Learning attentive pairwise interaction for fine-grained classification. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 13130–13137 (2020)
Zhao, B., Feng, J., Wu, X., Yan, S.: A survey on deep learning-based fine-grained object classification and semantic segmentation. Int. J. Autom. Comput. 14(2), 119–135 (2017)
Wang, Y., Morariu, V.I., Davis, L.S.: Learning a discriminative filter bank within a CNN for fine-grained recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4148–4157 (2018)
Yang, Z., Luo, T., Wang, D., Hu, Z., Gao, J., Wang, L.: Learning to navigate for fine-grained classification. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 420–435 (2018)
Oh Song, H., Xiang, Y., Jegelka, S., Savarese, S.: Deep metric learning via lifted structured feature embedding. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4004–4012 (2016)
Kaya, M., Bilge, H.Ş: Deep metric learning: a survey. Symmetry 11(9), 1066 (2019)
Huang, S., Wang, X., Tao, D.: Stochastic partial swap: enhanced model generalization and interpretability for fine-grained recognition. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 620–629 (2021)
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)
Tan, M., Le, Q.: EfficientNet: rethinking model scaling for convolutional neural networks. In: International Conference on Machine Learning, pp. 6105–6114. PMLR (2019)
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C.: MobileNetv 2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4510–4520 (2018)
Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2015)
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)
Liu, Z., Mao, H., Wu, C.Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022)
Hu, T., Qi, H., Huang, Q., Lu, Y.: See better before looking closer: weakly supervised data augmentation network for fine-grained visual classification. arXiv preprint arXiv:1901.09891 (2019)
Chattopadhay, A., Sarkar, A., Howlader, P., Balasubramanian, V.N.: Grad-CAM++: generalized gradient-based visual explanations for deep convolutional networks. In: 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 839–847. IEEE (2018)
Kuo, M.T., et al.: A deep learning approach in diagnosing fungal keratitis based on corneal photographs. Sci. Rep. 10(1), 14424 (2020)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Lin, K., Zhang, J., Jiang, X., Liu, J., Zhou, S. (2023). BF-Net: A Fine-Grained Network for Identify Bacterial and Fungal Keratitis. In: Iliadis, L., Papaleonidas, A., Angelov, P., Jayne, C. (eds) Artificial Neural Networks and Machine Learning – ICANN 2023. ICANN 2023. Lecture Notes in Computer Science, vol 14257. Springer, Cham. https://doi.org/10.1007/978-3-031-44216-2_5
Download citation
DOI: https://doi.org/10.1007/978-3-031-44216-2_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-44215-5
Online ISBN: 978-3-031-44216-2
eBook Packages: Computer ScienceComputer Science (R0)