Abstract
This paper aims to build a hybrid convolutional neural architecture by leveraging the power of pre-trained ResNet 50 (trained on ImageNet dataset) through transfer learning. The proposed work has achieved state-of-the-art performance metrics on the BreakHis dataset, containing microscopic histopathological images of benign and malignant breast tumours. The model incorporates global average pooling, dropout and batch normalisation layers on top of the pre-trained ResNet50 backbone. This methodical superimposing of the GAP layer in tandem with Resnet50’s knowledge and training is the proposed novelty taking our model the extra mile. As a result, the binary classification problem between benign and malignant tumours is handled gracefully by our proposed architecture despite the target imbalance. We achieve an AUC of 0.946 and an accuracy of 98.7% which is better than the previously stated standard.
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
Deniz, E., Şengür, A., Kadiroğlu, Z., Guo, Y., Bajaj, V., Budak, Ü.: Transfer learning based histopathologic image classification for breast cancer detection. Health Inf. Sci. Syst. 6(1), 1–7 (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)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Adv. Neural Inf. Process. Syst. 25 (2012)
LeCun, Y., Bottou, L´., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)
Liang, H., Fu, W., Yi, F.: A survey of recent advances in transfer learning. In: 2019 IEEE 19th International Conference on Communication Technology (ICCT), pp. 1516–1523. IEEE (2019)
Nagpure, R., Chandak, S., Pathak, N.: Breast cancer detection using neural network mammogram. In: 2020 International Conference on Convergence to Digital World Quo Vadis (ICCDW), pp. 1–6 (2020)
Parvin, F., Al Mehedi Hasan, Md.: A comparative study of different types of convolutional neural networks for breast cancer histopathological image classification. In: 2020 IEEE Region 10 Symposium (TENSYMP), pp. 945–948. IEEE (2020)
Samanta, P., Raipuria, G., Singhal, N.: Context aggregation network for semantic labeling in histopathology images. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 673–676 (2021)
Shahidi, F.: Breast cancer histopathology image super-resolution using wide-attention gan with improved wasserstein gradient penalty and perceptual loss. IEEE Access 9, 32795–32809 (2021)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
Israk Soumik, M.F., Bin Aziz, A.Z., Ali Hossain, Md.: Improved transfer learning based deep learning model for breast cancer histopathological image classification. In: 2021 International Conference on Automation, Control and Mechatronics for Industry 4.0 (ACMI), pp. 1–4. IEEE (2021)
Israk Soumik, M.F., Ali Hossain, Md.: Brain tumor classification with inception network based deep learning model using transfer learning. In: 2020 IEEE Region 10 Symposium (TENSYMP), pp. 1018–1021. IEEE (2020)
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 (2015)
Spanhol, F.A., Oliveira, L.S., Petitjean, C., Heutte, L.: Breast cancer histopathological image classification using convolutional neural networks. In: 2016 International Joint Conference on Neural Networks (IJCNN), pp. 2560–2567. IEEE (2016)
Tripathi, S., Singh, S.: Histopathological image classification: Defying deep architectures on complex data. In: Santosh, K.C., Hegadi, R.S. (eds.) RTIP2R 2018. CCIS, vol. 1036, pp. 361–370. Springer, Singapore (2019). https://doi.org/10.1007/978-981-13-9184-2_33
Tripathi, S., Singh, S.K.: Cell nuclei classification in histopathological images using hybrid OL convent. ACM Trans. Multim. Comput. Commun. Appl. 16(1s), 1–22 (2020)
Wei, B., Han, Z., He, X., Yin, Y.: Deep learning model based breast cancer histopathological image classification. In: 2017 IEEE 2nd International Conference on Cloud Computing and Big Data Analysis (ICCCBDA), pp. 348–353. IEEE (2017)
Xiang, Z., Ting, Z., Weiyan, F., Cong, L.: Breast cancer diagnosis from histopathological image based on deep learning. In: 2019 Chinese Control and Decision Conference (CCDC), pp. 4616–4619. IEEE (2019)
Xie, P., Li, T., Li, F., Zuo, K., Zhou, J., Liu, J.: Multi-scale convolutional neural network for melanoma histopathology image classification. In: 2021 IEEE 3rd International Conference on Frontiers Technology of Information and Computer (ICFTIC), pp. 551–554 (2021)
Zhuang, F., et al.: A comprehensive survey on transfer learning. Proc. IEEE 109(1), 43–76 (2020)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Dubey, A., Singh, S.K., Jiang, X. (2022). Leveraging CNN and Transfer Learning for Classification of Histopathology Images. In: Khare, N., Tomar, D.S., Ahirwal, M.K., Semwal, V.B., Soni, V. (eds) Machine Learning, Image Processing, Network Security and Data Sciences. MIND 2022. Communications in Computer and Information Science, vol 1763. Springer, Cham. https://doi.org/10.1007/978-3-031-24367-7_1
Download citation
DOI: https://doi.org/10.1007/978-3-031-24367-7_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-24366-0
Online ISBN: 978-3-031-24367-7
eBook Packages: Computer ScienceComputer Science (R0)