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TR-BI-RADS: a novel dataset for BI-RADS based mammography classification

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Abstract

Breast cancer is still a crucial public health problem worldwide, especially among women. Early diagnosis and treatment can be provided to patients with regular mammography. The BI-RADS system, which is a standard approach used when interpreting mammography results, is widely used worldwide. The number of datasets classified according to the BI-RADS system is mostly limited. Based on this shortcoming, in this study, we introduce a new benchmark dataset, "TR-BI-RADS", for mammogram classification based on BI-RADS standardization. A convolution neural network (CNN) is evaluated on this dataset. In addition to the newly defined (TR-BI-RADS) dataset, experiments are also carried out on the other dataset (INbreast Dataset), available in the literature and consists of BI-RADS categories. We believe that the TR-BI-RADS dataset will be beneficial for detecting breast cancer in the future studies.

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Data available on request from the authors.

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Authors and Affiliations

  1. Health Information Systems, Republic of Turkey, Ministry of Health, Ankara, Turkey

    Mustafa Mahir Ülgü & Yasin Yarbay

  2. Department of Computer Engineering, Hacettepe University, Ankara, Turkey

    Cemil Zalluhoglu & Ebru Akcapinar Sezer

  3. Republic of Turkey Ministry of Health, Ankara, Turkey

    Suayip Birinci

Authors
  1. Mustafa Mahir Ülgü
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  2. Cemil Zalluhoglu
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  3. Suayip Birinci
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  4. Yasin Yarbay
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  5. Ebru Akcapinar Sezer
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Correspondence to Cemil Zalluhoglu.

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Ülgü, M.M., Zalluhoglu, C., Birinci, S. et al. TR-BI-RADS: a novel dataset for BI-RADS based mammography classification. Neural Comput & Applic 36, 3699–3709 (2024). https://doi.org/10.1007/s00521-023-09251-z

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  • DOI: https://doi.org/10.1007/s00521-023-09251-z

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