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Automatic classification of clustered microcalcifications in digitized mammogram using ensemble learning
Author(s): Nashid Alam; Reyer Zwiggelaar
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Paper Abstract

Microcalcifications (MC) are small deposits of calcium, which are associated with early signs of breast cancer. In this paper, a novel approach is presented to develop a computer-aided diagnosis (CADx) system for automatic differentiation between benign and malignant MC clusters based on their morphology, texture, and the distribution of individual and global features using an ensemble classifier. The images were enhanced, segmented and the feature extraction and selection phase were carried out to generate the feature space which was later fed into an ensemble classifier to classify the MC clusters. The validity of the proposed method was investigated by using two well-known digitized datasets that contain biopsy proven results for MC clusters: MIAS (24 images: 12 benign, 12 malignant) and DDSM (280 images: 148 benign and 132 malignant). A high classification accuracies (100% for MIAS and 91.39% for DDSM) and good ROC results (area under the ROC curve equal to 1 for MIAS and 0.91 for DDSM) were achieved. A full comparison with related publications is provided. The results indicate that the proposed approach is outperforming the current state-of-the-art methods.

Paper Details

Date Published: 6 July 2018
PDF: 8 pages
Proc. SPIE 10718, 14th International Workshop on Breast Imaging (IWBI 2018), 1071816 (6 July 2018); doi: 10.1117/12.2315375
Show Author Affiliations
Nashid Alam, Aberystwyth Univ. (United Kingdom)
Reyer Zwiggelaar, Aberystwyth Univ. (United Kingdom)

Published in SPIE Proceedings Vol. 10718:
14th International Workshop on Breast Imaging (IWBI 2018)
Elizabeth A. Krupinski, Editor(s)

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