Abstract
Screening of breast cancer from histology images is a popular research problem in medical imaging. Most of the methods in recent days used deep learning models for predicting the same. But, at times, such methods dealt with not only higher-dimensional features but also may suffer from containing irrelevant and sometimes redundant features. To overcome this shortcoming, in the present work, we employ a popularly used particle swarm optimization (PSO) algorithm to obtain the near-optimal feature set. To extract the features from images we first preprocess the images to obtain stain normalized images and then pass them through a pre-trained MobileNet model for extracting the features. We have evaluated our model on a recent dataset, published through ICIAR BACH 2018 grand challenge. The experimental results show an improvement of 6.25% recognition accuracy with around 54% reduced features. We have also compared our result with two state-of-the-art CNN models: InceptionResNet and DenseNet, and we found that the use of MobileNet is better. The capability of the present model is comparable with some state-of-the-art methods on the BACH dataset.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Aresta G et al (2019) Bach: grand challenge on breast cancer histology images. Med Image Anal 56:122–139
Brancati N, Frucci M, Riccio D (2018) Multi-classification of breast cancer histology images by using a ne-tuning strategy. In: International conference image analysis and recognition. Springer, pp 771–778. https://doi.org/10.1007/978-3-319-93000-8_87
Chennamsetty SS, Safwan M, Alex V (2018) Classification of breast cancer histology image using ensemble of pre-trained neural networks. In: International conference image analysis and recognition. Springer, pp 804–811. https://doi.org/10.1007/978-3-319-93000-8_91
Guha R et al (2021) PyF S : A python package for feature selection using meta heuristic optimization algorithms. In: Accepted in 3rd international conference on computational intelligence in pattern recognition (CIPR-2021). Springer
Howard AG et al (2017) Mobilenets: efficient convolutional neural networks for mobile vision applications. In: arXiv preprint arXiv:1704.04861
Huang G et al (2017) Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4700–4708. https://doi.org/10.1109/CVPR.2017.243
ICIAR 2018 grand challenge on breast cancer histology images. https://iciar2018-challenge.grand-challenge.org/
Koné I, Boulmane L (2018) Hierarchical resnext models for breast cancer histology image classification. In: International conference image analysis and recognition. Springer, pp 796–803. https://doi.org/10.1007/978-3-319-93000-8_90
Koohi I, Groza VZ (2014) Optimizing particle swarm optimization algorithm. In: 2014 IEEE 27th Canadian conference on electrical and computer engineering (CCECE), pp 1–5. https://doi.org/10.1109/CCECE. 2014.6901057
Macenko M (2009) A method for normalizing histology slides for quantitative analysis. In: IEEE international symposium on biomedical imaging: from nano to macro. IEEE, 1107–1110
Rakhlin A et al (2018) Deep convolutional neural networks for breast cancer histology image analysis. In: International conference image analysis and recognition. Springer, pp 737–744. https://doi.org/10.1007/978-3-319-93000-8_83
Roy K et al (2019) Patch-based system for classification of breast histology images using deep learning. In: Computerized medical imaging and graphics 71, pp 90–103. ISSN: 0895-6111. https://doi.org/10.1016/j.compmedimag.2018.11.003. https://www.sciencedirect.com/science/article/pii/S0895611118302039
Sanyal R, Kar D, Sarkar R (2021) Carcinoma type classification from high-resolution breast microscopy images using a hybrid ensemble of deep convolutional features and gradient boosting trees classifiers. In: IEEE/ACM transactions on computational biology and bioinformatics, pp 1–1. https://doi.org/10.1109/TCBB.2021.3071022
Sarkar S et al (2018) An advanced particle swarm optimization based feature selection method for tri-script handwritten digit recognition. In: International conference on computational intelligence, communications, and business analytics. Springer, pp 82–94
Wang Z et al (2018) Classification of breast cancer histopathological images using convolutional neural networks with hierarchical loss and global pooling. In: International conference image analysis and recognition. Springer, pp 745–753. https://doi.org/10.1007/978-3-319-93000-8_84
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Das, S., Chatterjee, A., Dey, S., Saha, S., Malakar, S. (2023). Breast Cancer Detection from Histology Images Using Deep Feature Selection. In: Basu, S., Kole, D.K., Maji, A.K., Plewczynski, D., Bhattacharjee, D. (eds) Proceedings of International Conference on Frontiers in Computing and Systems. Lecture Notes in Networks and Systems, vol 404. Springer, Singapore. https://doi.org/10.1007/978-981-19-0105-8_31
Download citation
DOI: https://doi.org/10.1007/978-981-19-0105-8_31
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-0104-1
Online ISBN: 978-981-19-0105-8
eBook Packages: EngineeringEngineering (R0)