Advertisement

Detection of Necrosis in Mice Liver Tissue Using Deep Convolutional Neural Network

  • Nilanjana Dutta Roy
  • Arindam BiswasEmail author
  • Souvik Ghosh
  • Rajarshi Lahiri
  • Abhijit Mitra
  • Manabendra Dutta Choudhury
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11942)

Abstract

Acute Hepatic Necrosis is an early sign of liver dysfunction. Liver dysfunction is one of the major reasons for increasing death rate. Accurate diagnosis in less time, along with proper medication, show a ray of hope in controlling the aggravation of the situation. To overcome the unfavorable effects of harmful drugs, medicinal plant extract has become major thrust area nowadays. This research work has presented a way to show the improvements in mice liver tissue after applying the designated composition of the plant extract. And the performance is measured with our designed deep convolutional neural network (CNN) architecture along with the preprocessing techniques that has shown to be competent to classify microscopic images of mice hepatic tissues. Considering a small database of 30 images, we introduced a preprocessing stage which included the dividing of the original microscopic images to small patches. The accuracy of the classification results using the proposed CNN based classifier was 99.33%.

Keywords

Hepatic necrosis Medicinal plant extract Medical imaging Deep learning Computer vision 

Notes

Acknowledgment

The authors would like to take this opportunity to thank the Department of Biotechnology, Govt. of India for funding this research.

References

  1. 1.
    Bhardwaj, A., Khatri, P., Soni, M.: Potent herbal hepatoprotective drugs: a review. J. Adv. Sci. Res. 2(2), 15–20 (2011)Google Scholar
  2. 2.
    Dhiman, A., Nanda, A., Ahmad, S.: A recent update in research on the antihepatotoxic potential of medicinal plants. J. Chin. Integr. Med. 10, 117–119 (2012)CrossRefGoogle Scholar
  3. 3.
    Ashour, A.S., et al.: Light microscopy image de-noising using optimized LPA-ICI filter. Neural Comput. Appl. 29(12), 1517–1533 (2018)CrossRefGoogle Scholar
  4. 4.
    Chakraborty, S., et al.: Modified cuckoo search algorithm in microscopic image segmentation of hippocampus. Microsc. Res. Tech. 80(10), 1051–1072 (2017)CrossRefGoogle Scholar
  5. 5.
    Ding, H.: The Chinese Medicinal Crytogam, p. 104. Shanghai Publishing House of Science and Technology, Shanghai (1982)Google Scholar
  6. 6.
    Herz, W., Falk, H.: Progress in the Chemistry of Organic Natural Product. Springer, New York (1988).  https://doi.org/10.1007/978-3-7091-6507-2CrossRefGoogle Scholar
  7. 7.
    Janowczyk, A., Madabhushi, A.: Deep learning for digital pathology image analysis: a comprehensive tutorial with selected use cases. J. Pathol. Inform. 7, 29 (2016)CrossRefGoogle Scholar
  8. 8.
    Kainz, P., Pfeiffer, M., Urschler, M.: Segmentation and classification of colon glands with deep convolutional neural networks and total variation regularization. Peer J. 5, e3874 (2017)CrossRefGoogle Scholar
  9. 9.
    Lakhani, P.: Deep convolutional neural networks for endotracheal tube position and x-ray image classification: challenges and opportunities. J. Digit. Imaging 30(4), 460–468 (2017)CrossRefGoogle Scholar
  10. 10.
    Li, Q., Cai, W., Wang, X., Zhou, Y., Feng, D.D., Chen, M.: Medical image classification with convolutional neural network. In: 2014 13th International Conference on Control Automation Robotics & Vision (ICARCV), pp. 844–848. IEEE (2014)Google Scholar
  11. 11.
    Li, Z., et al.: Convolutional neural network based clustering and manifold learning method for diabetic plantar pressure imaging dataset. J. Med. Imaging Health Inform. 7(3), 639–652 (2017)CrossRefGoogle Scholar
  12. 12.
    Nirmala, M., Girija, K., Lakshman, K., Divya, T.: Hepatoprotective activity of musa paradisiaca on experimental animal models. Asian Pac. J. Trop. Biomed. 10, 11–15 (2012)CrossRefGoogle Scholar
  13. 13.
    Pan, X., et al.: Cell detection in pathology and microscopy images with multi-scale fully convolutional neural networks. World Wide Web 21(6), 1721–1743 (2018)CrossRefGoogle Scholar
  14. 14.
    Santosh, K., Alam, N., Roy, P.P., Wendling, L., Antani, S., Thoma, G.R.: A simple and efficient arrowhead detection technique in biomedical images. Int. J. Pattern Recogn. Artif. Intell. 30(05), 1657002 (2016)CrossRefGoogle Scholar
  15. 15.
    Shil, S., Dutta Choudhury, M.: Ethnomedicinal importance of pteridophytes used by reang tribe of Tripura, North East India. Ethnobot. Leaflets 13, 634–643 (2009)Google Scholar
  16. 16.
    Shin, H.C., et al.: Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans. Med. Imaging 35(5), 1285–1298 (2016)CrossRefGoogle Scholar
  17. 17.
    Suganya, R., Rajaram, S.: An efficient categorization of liver cirrhosis using convolution neural networks for health informatics. Cluster Comput. 22, 1–10 (2017)Google Scholar
  18. 18.
    Wang, Y., et al.: Classification of mice hepatic granuloma microscopic images based on a deep convolutional neural network. Appl. Soft Comput. 74, 40–50 (2019)CrossRefGoogle Scholar
  19. 19.
    Xu, J., Luo, X., Wang, G., Gilmore, H., Madabhushi, A.: A deep convolutional neural network for segmenting and classifying epithelial and stromal regions in histopathological images. Neurocomputing 191, 214–223 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Nilanjana Dutta Roy
    • 1
  • Arindam Biswas
    • 1
    Email author
  • Souvik Ghosh
    • 2
  • Rajarshi Lahiri
    • 2
  • Abhijit Mitra
    • 3
  • Manabendra Dutta Choudhury
    • 3
  1. 1.Indian Institute of Engineering Science and Technology, ShibpurShibpurIndia
  2. 2.Department of Computer Science and EngineeringInstitute of Engineering and ManagementKolkataIndia
  3. 3.Department of Life Science and BioinformaticsAssam UniversitySilcharIndia

Personalised recommendations