Abstract
Early detection of tumor tissue leading to cancer is the most burning health issue of the present world due to an increase of radiation, ultraviolet light, radon gas, infectious agents etc. To diagnose the tumor cell promptly nowadays computer-aided detection (CAD) systems using a convolutional neural network (CNN) draws a significant role in the health sector. Many complicated CNN model has been introduced to effectively classify tumor cell but in this study, we proposed a relatively less complex deep learning approach that is as effective and reliable as a renowned pre-trained models such as VGG19, Inception-v3, Resnet-50 and DenseNet-201. Our proposed architecture can perfectly classify the tumor cell based on the PatchCamelyon (PCam) dataset with appeasement validation accuracy 94.70% using less computational parameters, comparatively mentioned pre-trained model.
For writing (original draft preparation) include Renu Gupta with other authors.
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
Cancer. www.who.int/health-topics/cancer. Accessed 15 Jan 2020
What is cancer?. www.cancer.gov/about-cancer/understanding/what-is-cancer. Accessed 19 Jan 2020
Bakator, M., Radosav, D.: Deep learning and medical diagnosis: a review of literature. Multimodal Technol. Interact. 2(3), 47 (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)
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)
Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167 (2015)
Kassani, S.H., Kassani, P.H., Wesolowski, M.J., Schneider, K.A., Deters, R.: Classification of histopathological biopsy images using ensemble of deep learning networks. arXiv preprint arXiv:1909.11870 (2019)
Kononenko, I.: Machine learning for medical diagnosis: history, state of the art and perspective. Artif. Intell. Med. 23(1), 89–109 (2001)
Lantang, O., Tiba, A., Hajdu, A., Terdik, G.: Convolutional neural network for predicting the spread of cancer. In: 2019 10th IEEE International Conference on Cognitive Infocommunications (CogInfoCom), pp. 175–180. IEEE (2019)
Liang, Y., Yang, J., Quan, X., Zhang, H.: Metastatic breast cancer recognition in histopathology images using convolutional neural network with attention mechanism. In: 2019 Chinese Automation Congress (CAC), pp. 2922–2926. IEEE (2019)
Roy, K., Banik, D., Bhattacharjee, D., Nasipuri, M.: Patch-based system for classification of breast histology images using deep learning. Comput. Med. Imaging Graph. 71, 90–103 (2019)
Shen, D., Wu, G., Suk, H.I.: Deep learning in medical image analysis. Annu. Rev. Biomed. Eng. 19, 221–248 (2017)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
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)
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818–2826 (2016)
Veeling, B.S., Linmans, J., Winkens, J., Cohen, T., Welling, M.: Rotation equivariant CNNs for digital pathology. CoRR abs/1806.03962 (2018). arxiv.org/abs/1806.03962
Vo, D.M., Nguyen, N.Q., Lee, S.W.: Classification of breast cancer histology images using incremental boosting convolution networks. Inf. Sci. 482, 123–138 (2019)
Wang, D., Khosla, A., Gargeya, R., Irshad, H., Beck, A.H.: Deep learning for identifying metastatic breast cancer. arXiv preprint arXiv:1606.05718 (2016)
Zheng, Z., Zhang, H., Li, X., Liu, S., Teng, Y.: ResNet-based model for cancer detection. In: 2021 IEEE International Conference on Consumer Electronics and Computer Engineering (ICCECE), pp. 325–328. IEEE (2021)
Acknowledgement
The authors Debashis Gupta, Syed Rahat Hassan, and Urmi Saha sincerely thank Dr. Renu Gupta for sharing knowledge on tumor tissue and validating the proposed model’s predictions. Additionally, the authors also possess their gratitude to Dr. Engr. Mohammed Sowket Ali for his continual supervision.
Author information
Authors and Affiliations
Contributions
Conceptualization and Methodology - Debashis Gupta, Urmi Saha, Syed Rahat Hassan, and Renu Gupta. Implementation Coding - Debashis Gupta, Urmi Saha, and Syed Rahat Hassan. Resource Management - Debashis Gupta, Renu Gupta and Urmi Saha. Writing - orginal draft preparation - Debashis Gupta, Syed Rahat Hassan and Urmi Saha. Writing - Review and Editing -Renu Gupta, Mohammed Sowket Ali, and Debashis Gupta. All authors have read and agreed to the published version of the manuscript.
Corresponding author
Editor information
Editors and Affiliations
Ethics declarations
Conflicts of Interest
The authors declare no conflict of interest.
Rights and permissions
Copyright information
© 2023 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Gupta, D., Hassan, S.R., Gupta, R., Saha, U., Ali, M.S. (2023). Classification of Tumor Cell Using a Naive Convolutional Neural Network Model. In: Satu, M.S., Moni, M.A., Kaiser, M.S., Arefin, M.S. (eds) Machine Intelligence and Emerging Technologies. MIET 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 490. Springer, Cham. https://doi.org/10.1007/978-3-031-34619-4_14
Download citation
DOI: https://doi.org/10.1007/978-3-031-34619-4_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-34618-7
Online ISBN: 978-3-031-34619-4
eBook Packages: Computer ScienceComputer Science (R0)