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

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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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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