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

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 num...

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Bibliographic Details
Published in:Neural computing & applications 2024-03, Vol.36 (7), p.3699-3709
Main Authors: Ülgü, Mustafa Mahir, Zalluhoglu, Cemil, Birinci, Suayip, Yarbay, Yasin, Sezer, Ebru Akcapinar
Format: Article
Language:English
Subjects:
Artificial Intelligence
Artificial neural networks
Biopsy
Breast cancer
Classification
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Data Mining and Knowledge Discovery
Datasets
Image Processing and Computer Vision
Information storage
Machine learning
Mammography
Medical diagnosis
Medical screening
Original Article
Probability and Statistics in Computer Science
Public health
Software
Womens health
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Summary: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.
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-023-09251-z