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Deep learning versus classical neural approach to mammogram recognition

J. Kurek B. Świderski S. Osowski M. Kruk W. Barhoumi
Bulletin of the Polish Academy of Sciences Technical Sciences | 2018 | 66 | No 6 (Special Section on Deep Learning: Theory and Practice) | 831-840 | DOI: 10.24425/bpas.2018.125930
Keywords convolutional neural networks breast cancer diagnosis mammogram recognition diagnostic features
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Authors and Affiliations

J. Kurek
B. Świderski
S. Osowski
M. Kruk
W. Barhoumi

Fusion of feature selection methods in gene recognition

Fabian Gil Stanislaw Osowski
Bulletin of the Polish Academy of Sciences Technical Sciences | 2021 | 69 | 3 | e136748 | DOI: 10.24425/bpasts.2021.136748
Keywords diagnostic features selection methods genes recognition biomarkers
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Abstract

The paper presents the fusion approach of different feature selection methods in pattern recognition problems. The following methods are examined: nearest component analysis, Fisher discriminant criterion, refiefF method, stepwise fit, Kolmogorov-Smirnov criteria, T2-test, Kruskall-Wallis test, feature correlation with class, and SVM recursive feature elimination. The sensitivity to the noisy data as well as the repeatability of the most important features are studied. Based on this study, the best selection methods are chosen and applied in the process of selection of the most important genes and gene sequences in a dataset of gene expression microarray in prostate and ovarian cancers. The results of their fusion are presented and discussed. The small selected set of such genes can be treated as biomarkers of cancer.
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Authors and Affiliations

Fabian Gil
1
Stanislaw Osowski
1 2
ORCID: ORCID
  1. Warsaw University of Technology, Pl. Politechniki 1, 00-661 Warsaw, Poland
  2. Military University of Technology, ul. gen. Sylwestra Kaliskiego 2, 00-908 Warsaw, Poland

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