A Short Breast Imaging Reporting and Data System-Based Description for Classification of Breast Mass Grade

A Short Breast Imaging Reporting and Data System-Based Description for Classification of Breast Mass Grade

Identifying breast masses is relevant in early cancer detection. Automatic identification using computational methods helps assist medical experts with this task. Although high values have been reported in breast mass classification from digital mammograms, most results have focused on a general ben...

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Bibliographic Details
Main Authors: Jonas Grande-Barreto, Gabriela C. Lopez-Armas, Jose Antonio Sanchez-Tiro, Hayde Peregrina-Barreto
Format: Article
Language:English
Published: MDPI AG 2024-12-01
Series:Life
Subjects:
breast masses
BI-RADS grade
automatic classification
mass characterization
Online Access:https://www.mdpi.com/2075-1729/14/12/1634
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Summary:Identifying breast masses is relevant in early cancer detection. Automatic identification using computational methods helps assist medical experts with this task. Although high values have been reported in breast mass classification from digital mammograms, most results have focused on a general benign/malignant classification. According to the BI-RADS standard, masses are associated with cancer risk by grade depending on their specific shape, margin, and density characteristics. This work presents a methodology of testing several descriptors on the INbreast dataset, finding those better related to clinical assessment. The analysis provides a description based on BI-RADS for mass classification by combining neural networks and image processing. The results show that masses associated with grades BI-RADS-2 to BI-RADS-5 can be identified, reaching a general accuracy and sensitivity of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0.88</mn><mo>±</mo><mn>0.07</mn></mrow></semantics></math></inline-formula>. While this initial study is limited to a single dataset, it demonstrates the possibility of generating a description for automatic classification that is directly linked to the information analyzed by medical experts in clinical practice.
ISSN:2075-1729

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