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MRFE-CNN: multi-route feature extraction model for breast tumor segmentation in Mammograms using a convolutional neural network

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Abstract

Breast cancer is cancer that develops from the breast tissue and has been recognized as one of the most dangerous and deadly diseases that is the second leading cause of cancer deaths in women. To help doctors and radiologists to diagnose these tumors as well as decrease the time and increase the accuracy, many machine learning methods have been implemented by now. Most of these methods suffer from extracting some significant features that represent the boundary of tumors. This is due to the fact that benign and malignant tumors can be considered the same if some borders cannot segment properly. So, in this study, we propose an automatic breast tumor segmentation and recognition based on a shallow convolutional neural network that uses multi-feature extraction routes. Also, an image enhancement approach is used before applying the image into the model which leads to avoiding a very deep structure. Our strategy leads to improvement in detecting the border of tumors and boosts the classification accuracy of tumors. We evaluated our pipeline on Mammographic Image Analysis Society (Mini-MIAS) and Digital Database for Screening Mammography (DDSM) datasets. The developed model can localize and classify tumors with the accuracy of 0.936, 0.890, 0.871 on the DDSM, and 0.944, 0.915, 0.892 on the Mini-MIAS, for normal, benign, and malignant regions, respectively.

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Acknowledgements

The author Malika Bendechache is supported, in part, by Science Foundation Ireland (SFI) under the grants No. 13/RC/2094\_P2 (Lero) and 13/RC/2106\_P2 (ADAPT).

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The funding sources had no involvement in the study design, collection, analysis or interpretation of data, writing of the manuscript or in the decision to submit the manuscript for publication.

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Authors and Affiliations

  1. Department of Telecommunications Engineering, Faculty of Engineering, University of Guilan, Rasht, Iran

    Ramin Ranjbarzadeh

  2. Department of engineering, Tehran north Branch, Islamic Azad university, Tehran, Iran

    Nazanin Tataei Sarshar

  3. Faculty of Industrial Engineering, Urmia University of Technology, Urmia, Iran

    Saeid Jafarzadeh Ghoushchi

  4. Department of Computer Engineering, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran

    Mohammad Saleh Esfahani

  5. Department of Basic Science, Faculty of Basic Science, Central Tehran Branch, Islamic Azad University, Tehran, Iran

    Mahboub Parhizkar

  6. Department of Electrical Engineering, Urmia University of Technology (UUT), P.O. Box: 57166-419, Urmia, Iran

    Yaghoub Pourasad

  7. Department of Accounting, Economic and Financial Sciences, South Tehran Branch, Islamic Azad University, Tehran, Iran

    Shokofeh Anari

  8. School of Computing, Faculty of Engineering and Computing, Dublin City University, Dublin, Ireland

    Malika Bendechache

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  1. Ramin Ranjbarzadeh
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Ranjbarzadeh, R., Tataei Sarshar, N., Jafarzadeh Ghoushchi, S. et al. MRFE-CNN: multi-route feature extraction model for breast tumor segmentation in Mammograms using a convolutional neural network. Ann Oper Res 328, 1021–1042 (2023). https://doi.org/10.1007/s10479-022-04755-8

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