@article{6f8905bf6b65438881f1d516bcdaef89,
title = "Classification of microcalcification clusters in digital mammograms using a stack generalization based classifier",
abstract = "This paper presents a machine learning based approach for the discrimination of malignant and benign microcalcification (MC) clusters in digital mammograms. A series of morphological operations was carried out to facilitate the feature extraction from segmented microcalcification. A combination of morphological, texture, and distribution features from individual MC components and MC clusters were extracted and a correlation-based feature selection technique was used. The clinical relevance of the selected features is discussed. The proposed method was evaluated using three different databases: Optimam Mammography Image Database (OMI-DB), Digital Database for Screening Mammography (DDSM), and Mammographic Image Analysis Society (MIAS) database. The best classification accuracy (95.00 ± 0.57%) was achieved for OPTIMAM using a stack generalization classifier with 10-fold cross validation obtaining an A z value equal to 0.97 ± 0.01.",
keywords = "digital mammogram, microcalcification, stack generalization, classification, morphological features, Morphological features, Microcalcification, Stack generalization, Classification, Digital mammogram",
author = "Nashid Alam and Denton, {Erika R. E.} and Reyer Zwiggelaar",
note = "Funding Information: Funding: This work was supported by AberDoc Scholarship and President{\textquoteright}s Scholarship granted by Aberystwyth University to Nashid Alam. Funding Information: This work was supported by AberDoc Scholarship and President?s Scholarship granted by Aberystwyth University to Nashid Alam. We would like to express our deep gratitude to W. R. Ward, Honorary Senior Fellow, Institute of Veterinary Science, University of Liverpool, UK for his help in language editing, and proofreading. We are grateful to Neil Mac Parthalain, Aberystwyth University for his insight and discussions on stack generalization. We would also like to thank the anonymous reviewers for their insightful comments which led us to an improvement of the work. Publisher Copyright: {\textcopyright} 2019 by the authors.",
year = "2019",
month = sep,
day = "12",
doi = "10.3390/jimaging5090076",
language = "English",
volume = "5",
journal = "Journal of Imaging",
issn = "2313-433X",
publisher = "Multidisciplinary Digital Publishing Institute",
number = "9",
}