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Feature fusion based machine learning pipeline to improve breast cancer prediction

  • 1218: Engineering Tools and Applications in Medical Imaging
  • Published:
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Abstract

Early detection of malignant breast cancer can significantly improve the survival chances of the involved patients. Analysis of a non-invasive and non-radioactive modality like ultrasound imaging with the help of Machine Learning(ML) and Artificial Intelligence(AI) techniques can be crucial for achieving such effective early-stage detection of the disease. A feature fusion based approach is proposed in this work, in conjunction with an ML pipeline that systematically deals with various problems like high dimensionality, class imbalance, and hyperparameter tuning, so that efficient benign vs. malignant classification can be performed. Experimental evaluation on two publicly available datasets reveals that the proposed approach is able to outperform state-of-the-art techniques on the classification task with an overall performance of above 95% for all the evaluation metrics under consideration and an AUC of \(\sim \)0.99. More specifically, an overall improvement of (1-4)%, (2-10)% and (2-7)% over the current state-of-the-art approaches could be obtained for the Accuracy, AUC and Sensitivity metrics respectively, on both the datasets. Such an efficient approach can provide the necessary real-time decision support to the involved radiologists, making better cancer patient care possible.

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Code Availability

All the code corresponding to the experimental evaluation of the proposed approach will be made available at https://github.com/arnkmish.

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Acknowledgements

We thank Dr. Moi Hoon Yap, Reader in Computer Vision, Manchester Metropolitan University, Department of Computing, Mathematics and Digital Technology, John Dalton Building, Chester Street, Manchester M1 5GD, one of the PIs of the Breast Ultrasound Lesions Dataset(Dataset B) [69] for granting access to the UDIAT dataset to carry out academic research.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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Authors and Affiliations

  1. Department of CSE, National Institute of Technology Silchar, Silchar, India

    Arnab Kumar Mishra, Pinki Roy & Sivaji Bandyopadhyay

  2. Department of CSE, Bennett University, Greater Noida, India

    Sujit Kumar Das

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  1. Arnab Kumar Mishra
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Correspondence to Arnab Kumar Mishra.

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Data availability

The BUSI dataset utilized in this work for experimental evaluation purpose is a publicly available dataset introduced by [2]. The UDIAT dataset is another publicly available dataset utilized in this work which can be accessed by following official request procedure as suggested by the authors in [69].

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Mishra, A.K., Roy, P., Bandyopadhyay, S. et al. Feature fusion based machine learning pipeline to improve breast cancer prediction. Multimed Tools Appl 81, 37627–37655 (2022). https://doi.org/10.1007/s11042-022-13498-4

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