CFP last date
22 April 2024
Call for Paper
May Edition
IJCA solicits high quality original research papers for the upcoming May edition of the journal. The last date of research paper submission is 22 April 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

Semantic Segmentation of Cell Nuclei in Breast Cancer using Convolutional Neural Network

by Tomiya Said Ahmed Zarbega, Yasemin Gültepe
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 176 - Number 22
Year of Publication: 2020
Authors: Tomiya Said Ahmed Zarbega, Yasemin Gültepe
10.5120/ijca2020920133

Tomiya Said Ahmed Zarbega, Yasemin Gültepe . Semantic Segmentation of Cell Nuclei in Breast Cancer using Convolutional Neural Network. International Journal of Computer Applications. 176, 22 ( May 2020), 1-8. DOI=10.5120/ijca2020920133

@article{ 10.5120/ijca2020920133,
author = { Tomiya Said Ahmed Zarbega, Yasemin Gültepe },
title = { Semantic Segmentation of Cell Nuclei in Breast Cancer using Convolutional Neural Network },
journal = { International Journal of Computer Applications },
issue_date = { May 2020 },
volume = { 176 },
number = { 22 },
month = { May },
year = { 2020 },
issn = { 0975-8887 },
pages = { 1-8 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume176/number22/31328-2020920133/ },
doi = { 10.5120/ijca2020920133 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:43:10.725059+05:30
%A Tomiya Said Ahmed Zarbega
%A Yasemin Gültepe
%T Semantic Segmentation of Cell Nuclei in Breast Cancer using Convolutional Neural Network
%J International Journal of Computer Applications
%@ 0975-8887
%V 176
%N 22
%P 1-8
%D 2020
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Many studies have been carried out in the literature and practice by using deep learning technique and successful results have been obtained. Convolutional Neural Network (CNN), a specialized architecture of deep learning, is particularly successful in image processing. Semantic segmentation is a computer vision task to estimate pixel tags corresponding to the region to which it belongs or to the region of the surrounding region. Semantic segmentation aims to understand the class of special objects in the scene. In this paper, Convolutional Neural Network based on detection and semantic segmentation of cell nuclei for breast cancer was performed on the “PSB 2015 crowdsourced nuclei” data set. As a result, the CNN model gave the highest performance with precision (0.844), recall (0.832) and accuracy (0.851) compared to other classifiers in the literature and the most advanced methods.

References
  1. Gao, X., Li, W., Loomes, M., and Wang, L., 2017. A fused deep learning architecture for viewpoint classification of echocardiography, Information Fusion, vol. 36, 103-113.
  2. Shwendicke, F., Golla, T., Dreher, M., and Krois, J., 2019. Convolutional neural networks for dental image diagnostic: A scoping review, Journal of Dentistry, vol. 91.
  3. Voulodimos, A., Doulamis, N., Doulamis, A., and Protopapadakis, E., 2018. Deep learning for computer vision: A brief review. Computational Intelligence and Neuroscience, vol. 2018.
  4. Zhao, R., Yan, R., Chen, Z., Mao, K., Wang, P., and Gao, R. X., 2019. Mechanical systems and signal processing. Mechanical systems and signal processing, vol. 115, 213-237.
  5. Zeebaree, D. Q., Haron, H., Abdulazeez, A. M., and Zebari, D., A., 2019. Machine learning and region growing for breast cancer segmentation. International Conference on Advanced Science and Engineering.
  6. Rakhlin, A., Shvets, A., Iglovikov, V., and Kalinin, A. A. 2018. Deep convolutional neural networks for breast cancer histology image analysis. International Conference Image Analysis and Recognition, 737-744.
  7. Lévy, D., and Jain, A. 2016. Breast mass classification from mammograms using deep convolutional neural networks. arXiv preprint arXiv:1612.00542.
  8. Cruz-Roa, A. et al., 2014. Automatic detection of invasive ductal carcinoma in whole slide images with convolutional neural networks. Medical Imaging 2014, vol. 9041.
  9. Sirinukunwattana, K., et al., 2016. Locality sensitive deep learning for detection and classification of nuclei in routine colon cancer histology images. IEEE Trans. Med. Imaging, vol. 35, no. 5, 1196-1206.
  10. Wang, H., et al., 2014. Cascaded ensemble of convolutional neural networks and handcrafted features for mitosis detection. Medical Imaging 2014, vol. 9041.
  11. Cruz-Roa, A. A., Ovalle, J. E. A., Madabhushi, A., and Osorio, F. A. G., 2013. A deep learning architecture for image representation, visual interpretability and automated basal-cell carcinoma cancer detection. International Conference on Medical Image Computing and Computer-Assisted Intervention, 403-410.
  12. Wan, T., Cao, J., Chen, J., and Qin, Z., 2017. Automated grading of breast cancer histopathology using cascaded ensemble with combination of multi-level image features. Neurocomputing, vol. 229, 34-44.
  13. Cireşan, D. C., Giusti, A., Gambardella, L. M., and Schmidhuber, J., 2016. Mitosis detection in breast cancer histology images with deep neural networks. International Conference on Medical Image Computing and Computer-assisted Intervention, 411-418.
  14. Irshad, H., et al., 2014. Crowdsourcing image annotation for nucleus detection and segmentation in computational pathology: evaluating experts, automated methods, and the crowd. Pacific symposium on biocomputing Co-chairs, World Scientific, 294-305.
  15. Beck Lab., 2020. PSB Crowdsourced Nuclei Annotation, https://becklab.hms.harvard.edu/software/psb-crowdsourced-nuclei-annotation-data-1.
  16. Ghosh, S., Das, N., Das I., and Maulik, U., 2019. Understanding deep learning techniques for image segmentation. ACM Computing Surveys, vol. 52, no. 4.
  17. Fawcett, T., 2006. An introduction to ROC analysis. Pattern recognition letters, vol. 27, no. 8, 861-874.
  18. Story, M., and Congalton, R. G., 1986. Accuracy assessment: A user’s perspective. Photogramm Eng. Remote Sensing, vol. 52, no. 3, 397-399.
  19. Rahebi, J., and Hardalaç, F., 2014. Retinal blood vessel segmentation with neural network by using gray-level co-occurrence matrix-based features. Journal of medical systems, vol. 38, no. 8.
  20. Fraz, M. M., et al., 2012. Blood vessel segmentation methodologies in retinal images–a survey. Computer methods and programs in biomedicine, vol. 108, no. 1, 407-433.
  21. Diakogiannis, F., Waldner, F., Caccetta, P., and Wu, C., 2020. ResUNet-a: A deep learning framework for semantic segmentation of remotely sensed data. ISPRS Journal of Photogrammetry and Remote Sensing, vol. 162, 94-114.
  22. WR. Crum, Camara, O., and Hill, DL, 2006. Generalized overlap measures for evaluation and validation in medical image analysis. IEEE transactions on medical imaging, 1451-1461.
  23. Sudre, C. H., Wenqi, L., Vercauteren, T., Ourselin, S., and Cardoso, M. J., 2017. Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations. Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, 240-248.
  24. Kuse, M. Wang, Y.-F., Kalasannavar, V., Khan, M., and Rajpoot, N., 2011. Local isotropic phase symmetry measure for detection of beta cells and lymphocytes. Journal of pathology informatics, vol. 2.
  25. Ding, S., Li, H., Su, C., and Yu, J., 2013. Evolutionary artificial neural networks: A review. Artificial Intelligence Review, vol. 39, no. 3.
  26. Bengio, Y., Goodfellow, I., and Courville, A., 2016. Convolutional Networks. An MIT Prees Book, Chapter 9.
  27. Serra, J., 1986. Introduction to Mathematical Morphology. Computer Vision, Graphics and Image Processing, 62-66.
  28. Niea, F., Zhangb, P., Lia, J., and Din, D., 2017. A novel generalized entropy and its application in image thesholding. Signal Processing, 23-34.
  29. Karaboga, D., 2005. An idea based on honey bee swarm for numerical optimization, Technical Report TR06, Erciyes University, Engineering Faculty Computer Engineering Department.
  30. Xu, J., et al., 2015. Stacked sparse autoencoder (SSAE) for nuclei detection of breast cancer histopathology images, IEEE transactions on medical imaging, vol. 35, no. 1, 119-130.
  31. Yuan, Y., et al., 2012. Quantitative image analysis of cellular heterogeneity in breast tumors complements genomic profiling. Science translational medicine, vol. 4, no. 157.
  32. Xie, Y., Xing, F., Kong, X., Su, H., and Yang, L., 2015. Beyond classification: structured regression for robust cell detection using convolutional neural network. International Conference on Medical Image Computing and Computer-Assisted Intervention, 358-365.
Index Terms

Computer Science
Information Sciences

Keywords

Convolution neural network image segmentation semantic segmentation nuclei cells breast cancer.

Powered by PhDFocusTM