Building an ensemble system for diagnosing masses in mammograms

• Published in: International journal for computer assisted radiology and surgery Vol. 7; no. 2; pp. 323 - 329
• Main Authors: Zhang, Yu, Tomuro, Noriko, Furst, Jacob, Raicu, Daniela Stan
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer-Verlag 01.03.2012
• Subjects: Adult; Age Factors; Breast Diseases - diagnostic imaging; Breast Diseases - pathology; Breast Neoplasms - diagnosis; Breast Neoplasms - diagnostic imaging; Breast Neoplasms - pathology; Computer Imaging; Computer Science; Computer-Aided Design; Diagnosis, Differential; Female; Health Informatics; Humans; Imaging; Mammography - instrumentation; Mammography - methods; Medicine; Medicine & Public Health; Middle Aged; Original Article; Pattern Recognition and Graphics; Radiographic Image Interpretation, Computer-Assisted; Radiology; Reproducibility of Results; Surgery; Systems Analysis; Vision; Mass classification; Ensemble learning; Mass segmentation; CADx
• ISSN: 1861-6410; 1861-6429
• DOI: 10.1007/s11548-011-0628-7

Purpose Classification of a suspicious mass (region of interest, ROI) in a mammogram as malignant or benign may be achieved using mass shape features. An ensemble system was built for this purpose and tested. Methods Multiple contours were generated from a single ROI using various parameter settings of the image enhancement functions for the segmentation. For each segmented contour, the mass shape features were computed. For classification, the dataset was partitioned into four subsets based on the patient age (young/old) and the ROI size (large/small). We built an ensemble learning system consisting of four single classifiers, where each classifier is a specialist, trained specifically for one of the subsets. Those specialist classifiers are also an optimal classifier for the subset, selected from several candidate classifiers through preliminary experiment. In this scheme, the final diagnosis (malignant or benign) of an instance is the classification produced by the classifier trained for the subset to which the instance belongs. Results The Digital Database for Screening Mammography (DDSM) from the University of South Florida was used to test the ensemble system for classification of masses, which achieved a 72% overall accuracy. This ensemble of specialist classifiers achieved better performance than single classification (56%). Conclusion An ensemble classifier for mammography-detected masses may provide superior performance to any single classifier in distinguishing benign from malignant cases.