Abstract
Breast Cancer is the tumor that occurs most commonly in women and follows lung cancer in the most common cancers. Mortality rates caused by cancer can be reduced if early detection and treatment mechanisms are instituted. With recent advances in Deep Learning and Computer Vision, its application in diagnosis through pattern recognition is fast emerging. This paper compares the classification performance of two convolutional neural network models into benign and malignant subclasses for a breast cancer histopathological dataset. The first is built on Inception v3 architecture while the second contains residual connections in the Inception network called InceptionResNet v2. Performance has been enhanced by augmenting the data. Time taken to train and computational cost have been reduced through transfer learning. The results show accuracy from 85.9% to 91.3% on Inception v3 model. Inception Resnet v2 model performs better than Inception v3 with accuracies ranging from 89.8% to 94.6%.
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
References
Spanhol, F.A., Oliveira, L.S., Petitjean, C., Heutte, L.: A dataset for breast cancer histopathological image classification. IEEE Trans. Biomed. Eng. 63(7), 1455–1462 (2016)
Bardou, D., Zhang, K., Ahmad, S.M.: Classification of breast cancer based on histology images using convolutional neural networks. IEEE Access 6, 24680–24693 (2018)
Jannesari, M., et al.: Breast cancer histopathological image classification: a deep learning approach. In: IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid, Spain, pp. 2405–2412 (2018)
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016)
Szegedy, C., Ioffe, S., Vanhoucke, V.: Inception-v4, inception-ResNet and the impact of residual connections on learning. In: AAAI Conference on Artificial Intelligence (2016)
World Health Organization: Breast Cancer Prevention and Control. https://www.who.int/cancer/detection/breastcancer/en/. Accessed 27 Feb 2019
International Agency for Research on Cancer: Latest global cancer data: Cancer burden rises to 18.1 million new cases and 9.6 million cancer deaths in 2018. https://www.who.int/cancer/PRGlobocanFinal.pdf. Accessed 27 Feb 2019
World Health Organization: Classification of Tumours, Tumours of the Breast and female genital organs. https://www.iarc.fr/wp-content/uploads/2018/07/BB4.pdf. Accessed 27 Feb 2019
Makki, J.: Diversity of breast carcinoma: histological subtypes and clinical relevance. Clin. Med. Insights: Pathol. 8, 23–31 (2015)
Veta, M., Pluim, J.P.W., van Diest, P.J., Viergever, M.A.: Breast cancer histopathology image analysis: a review. IEEE Trans. Biomed. Eng. 61(5), 1400–1411 (2014)
Araújo, T., Aresta, G., Castro, E., Rouco, J., Aguiar, P., Eloy, C., et al.: Classification of breast cancer histology images using convolutional neural networks (2017)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. Commun. ACM 60(2), 84–90 (2012)
Lecun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)
Tajbakhsh, N., et al.: Convolutional neural networks for medical image analysis: full training or fine tuning? IEEE Trans. Med. Imaging 35(5), 1299–1312 (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Muralikrishnan, M., Anitha, R. (2020). Comparison of Breast Cancer Multi-class Classification Accuracy Based on Inception and InceptionResNet Architecture. In: Hemanth, D.J., Kumar, V.D.A., Malathi, S., Castillo, O., Patrut, B. (eds) Emerging Trends in Computing and Expert Technology. COMET 2019. Lecture Notes on Data Engineering and Communications Technologies, vol 35. Springer, Cham. https://doi.org/10.1007/978-3-030-32150-5_118
Download citation
DOI: https://doi.org/10.1007/978-3-030-32150-5_118
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-32149-9
Online ISBN: 978-3-030-32150-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)