Abstract
Tuberculosis (TB) is a global issue of public health. The paper presents the result of the investigation of the clinical efficacy of a convolutional neural network for detection of acid-fast stained TB bacillus. The experimental set contains images of the results of microscopy of patients' sputum stained by the Ziehl–Neelsen method. During the experiment, the original set of images segmented to augmentation the data. We built a few convolutional neural networks (CNN) models to recognize TB bacillus by transfer learning. The experiment conducted based on AlexNet, VGGNet-19, ResNet-18, DenseNet, GoogLeNet-incept-v3, In-ceptionResNet-v2 and the classic three-layer model. The DenseNet is the most productive model of transfer learning on the experimental set. During the study, the usual three-layer convolution network developed, which showed the maximum value of accuracy in the experiment. A convolutional neural network with a simple structure may be an effective base for an automated detection system for stained TB bacilli, but image segmentation is required to increase recognition accuracy.
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
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: GradientBased Learning Applied to Document Recognition. Proc. IEEE 86(11), 2278–2324 (1998)
Koch, M.L., Cote, R.A.: Comparison of fluorescence microscopy with Ziehl-Neelsen stain for demonstration of acid-fast bacilli in smear preparations and tissue sections. Am. Rev. Respir. Dis 91, 283–284 (1965)
Esteva, A., Kuprel, B., Novoa, R.A., Ko, J., Swetter, S.M., Blau, H.M., Thrun, S.: Dermatologist-level classification of skin cancer with deep neural networks. Nature 542(7639), 115 (2017)
Rajpurkar, P., Irvin, J., Zhu, K., Yang, B., Mehta, H., Duan, T., Ding, D., Bagul, A., Langlotz, C., Shpanskaya, K., Lungren, M.: A Radiologist-level pneumonia detection on chest x-rays with deep learning, Preprint arXiv:1711.05225 (2017)
Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., Summers, R.: Chestx-ray8: hospital-scale chest x-ray database and benchmarks on weaklysupervised classication and localization of common thorax diseases. In: 30th IEEE Conference on Computer Vision and Pattern Recognition 2017, Honolulu, Hawaii, pp. 3462–3471 (2017)
Alom, Z., Taha, T., Yakopcic, C., Westberg, S., Sidike P., Nasrin S., Van Esesn B., Awwal, A., Asari, V.: The history began from AlexNet: a comprehensive survey on deep learning approaches. Preprint arXiv:1803.01164 (2018)
Zhang, X., Zou, J., He, K., Sun, J.: Accelerating very deep convolutional networks for classification and detection. Preprint arXiv:1505.06798 (2015)
Zhang, A., Lipton, Z., Li, M., Smola, A.: Dive into Deep Learning. Preprint d2l.ai (2020)
Zhang, A., Lipton, Z., Li, M., Smola, A.: Densely connected convolutional networks. Preprint arXiv:1608.06993v3 (2016)
Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A.: Inception-v4, Inception-Resnet and the impact of residual connections on learning. In: Proceedings of 31st AAAI Conference Artificial Intelligence, pp. 4278–4284 (2017)
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: Proceedings of 28th IEEE Conference on Computer Vision and Pattern Recognition 2015, Boston, MA, pp. 1–9 (2015)
Narkevich, A.N., Shelomentseva, I.G., Vinogradov, K.A., Sysoev, S.A.: Comparison of segmentation methods for digital microscopic images of sputum stained by the Ziehl-Neelsen method. Eng. J. Don 4, 1–11 (2017)
Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016)
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. Preprint arXiv:1512.00567 (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Shelomentseva, I.G., Chentsov, S.V. (2021). Classification of Microscopy Image Stained by Ziehl–Neelsen Method Using Different Architectures of Convolutional Neural Network. In: Kryzhanovsky, B., Dunin-Barkowski, W., Redko, V., Tiumentsev, Y. (eds) Advances in Neural Computation, Machine Learning, and Cognitive Research IV. NEUROINFORMATICS 2020. Studies in Computational Intelligence, vol 925. Springer, Cham. https://doi.org/10.1007/978-3-030-60577-3_32
Download citation
DOI: https://doi.org/10.1007/978-3-030-60577-3_32
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-60576-6
Online ISBN: 978-3-030-60577-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)