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Context-based ensemble classification for the detection of architectural distortion in a digitised mammogram

Context-based ensemble classification for the detection of architectural distortion in a digitised mammogram

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The problem of computer-aided detection of architectural distortion (AD) in a digitised mammogram has been attempted in this manuscript. In examining a mammogram, the decision regarding a particular region of interest (RoI) is dependent on the appearance of the surrounding regions. However, in existing methods to detect AD the inference about an RoI is dependent on the appearance of this RoI alone. In addition, multiple radiologists infer the same mammogram in coming to a final decision about the mammogram. Contrary to popular ensemble classifiers like Adaboost and Random Forest, the authors propose an ensemble based method (imitating multiple radiologists by classifiers) for detecting AD such that the decision on a test RoI is dependent on the decisions of the surrounding RoIs in the proposed ensemble classifier. The proposed context-based ensemble classifier has been validated on two mammographic databases. The proposal shows promising results in both the databases.

http://iet.metastore.ingenta.com/content/journals/10.1049/iet-ipr.2019.0639
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