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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MDPI AG
2024-12-01
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| Series: | Life |
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| Online Access: | https://www.mdpi.com/2075-1729/14/12/1634 |
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| author | Jonas Grande-Barreto Gabriela C. Lopez-Armas Jose Antonio Sanchez-Tiro Hayde Peregrina-Barreto |
| author_facet | Jonas Grande-Barreto Gabriela C. Lopez-Armas Jose Antonio Sanchez-Tiro Hayde Peregrina-Barreto |
| author_sort | Jonas Grande-Barreto |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-4a52608cae2b43b9a74190e21e97c5c1 |
| institution | Kabale University |
| issn | 2075-1729 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Life |
| spelling | doaj-art-4a52608cae2b43b9a74190e21e97c5c12024-12-27T14:36:09ZengMDPI AGLife2075-17292024-12-011412163410.3390/life14121634A Short Breast Imaging Reporting and Data System-Based Description for Classification of Breast Mass GradeJonas Grande-Barreto0Gabriela C. Lopez-Armas1Jose Antonio Sanchez-Tiro2Hayde Peregrina-Barreto3Tecnologías de la Información, Universidad Politécnica de Puebla, Cuanalá, Puebla 72640, MexicoCentro de Enseñanza Técnica Industrial, C. Nueva Escocia 1885, Guadalajara 44638, MexicoInstituto Nacional de Astrofísica, Óptica y Electrónica, Luis Enrique Erro 1, San Andres Cholula 72840, MexicoInstituto Nacional de Astrofísica, Óptica y Electrónica, Luis Enrique Erro 1, San Andres Cholula 72840, MexicoIdentifying 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.https://www.mdpi.com/2075-1729/14/12/1634breast massesBI-RADS gradeautomatic classificationmass characterization |
| spellingShingle | Jonas Grande-Barreto Gabriela C. Lopez-Armas Jose Antonio Sanchez-Tiro Hayde Peregrina-Barreto A Short Breast Imaging Reporting and Data System-Based Description for Classification of Breast Mass Grade Life breast masses BI-RADS grade automatic classification mass characterization |
| title | A Short Breast Imaging Reporting and Data System-Based Description for Classification of Breast Mass Grade |
| title_full | A Short Breast Imaging Reporting and Data System-Based Description for Classification of Breast Mass Grade |
| title_fullStr | A Short Breast Imaging Reporting and Data System-Based Description for Classification of Breast Mass Grade |
| title_full_unstemmed | A Short Breast Imaging Reporting and Data System-Based Description for Classification of Breast Mass Grade |
| title_short | A Short Breast Imaging Reporting and Data System-Based Description for Classification of Breast Mass Grade |
| title_sort | short breast imaging reporting and data system based description for classification of breast mass grade |
| topic | breast masses BI-RADS grade automatic classification mass characterization |
| url | https://www.mdpi.com/2075-1729/14/12/1634 |
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