3 March 2009 Using three-class BANN classifier in the automated analysis of breast cancer lesions in DCE-MRI
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Proceedings Volume 7260, Medical Imaging 2009: Computer-Aided Diagnosis; 72600J (2009) https://doi.org/10.1117/12.813507
Event: SPIE Medical Imaging, 2009, Lake Buena Vista (Orlando Area), Florida, United States
Abstract
The purpose of this study is to investigate three-class Bayesian artificial neural networks (BANN) in dynamic contrastenhanced MRI (DCE-MRI) CAD in distinguishing different types of breast lesions including ductal carcinoma in situ (DCIS), invasive ductal carcinoma (IDC), and benign. The database contains 72 DCIS lesions, 124 IDC lesions, and 131 benign breast lesions (no cysts). Breast MR images were obtained with a clinical DCE-MRI scanning protocol. In 3D, we automatically segmented each lesion and calculated its characteristic kinetic curve using the fuzzy c-means method. Morphological and kinetic features were automatically extracted, and stepwise linear discriminant analysis was utilized for feature selection in four subcategories: DCIS vs. IDC, DCIS vs. benign, IDC vs. benign, and malignant (DCIS + IDC) vs. benign. Classification was automatically performed with the selected features for each subcategory using round-robin-by-lesion two-class BANN and three-class BANN. The performances of the classifiers were assessed with two-class ROC analysis. We failed to show any statistically significant differences between the two-class BANN and three-class BANN for all four classification tasks, demonstrating that the three-class BANN performed similarly to the two-class BANN. A three-class BANN is expected to be more desirable in the clinical arena for both diagnosis and patient management.
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Neha Bhooshan, Maryellen Giger, Darrin Edwards, Karen Drukker, Sanaz Jansen, Hui Li, Li Lan, Gillian Newstead, "Using three-class BANN classifier in the automated analysis of breast cancer lesions in DCE-MRI", Proc. SPIE 7260, Medical Imaging 2009: Computer-Aided Diagnosis, 72600J (3 March 2009); doi: 10.1117/12.813507; https://doi.org/10.1117/12.813507
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