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Breast Tumor Classification Based on a Computerized Breast Imaging Reporting and Data System Feature System
Author(s) -
Qiao Mengyun,
Hu Yuzhou,
Guo Yi,
Wang Yuanyuan,
Yu Jinhua
Publication year - 2018
Publication title -
journal of ultrasound in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.574
H-Index - 91
eISSN - 1550-9613
pISSN - 0278-4297
DOI - 10.1002/jum.14350
Subject(s) - medicine , breast imaging , breast cancer , artificial intelligence , classifier (uml) , feature (linguistics) , receiver operating characteristic , feature extraction , mammography , radiology , pattern recognition (psychology) , computer science , cancer , linguistics , philosophy
Objectives This work focused on extracting novel and validated digital high‐throughput features to present a detailed and comprehensive description of the American College of Radiology Breast Imaging Reporting and Data System (BI‐RADS) with the goal of improving the accuracy of ultrasound breast cancer diagnosis. Methods First, the phase congruency approach was used to segment the tumors automatically. Second, high‐throughput features were designed and extracted on the basis of each BI‐RADS category. Then features were selected based on the basis of a Student t test and genetic algorithm. Finally, the AdaBoost classifier was used to differentiate benign tumors from malignant ones. Results Experiments were conducted on a database of 138 pathologically proven breast tumors. The system was compared with 6 state‐of‐art BI‐RADS feature extraction methods. By using leave‐one‐out cross‐validation, our system achieved a highest overall accuracy of 93.48%, a sensitivity of 94.20%, a specificity of 92.75%, and an area under the receiver operating characteristic curve of 95.67%, respectively, which were superior to those of other methods. Conclusions The experiments demonstrated that our computerized BI‐RADS feature system was capable of helping radiologists detect breast cancers more accurately and provided more guidance for final decisions.