{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T05:11:49Z","timestamp":1775711509673,"version":"3.50.1"},"reference-count":57,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2021,11,27]],"date-time":"2021-11-27T00:00:00Z","timestamp":1637971200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Convolutional neural networks (CNNs) have gained prominence in the research literature on image classification over the last decade. One shortcoming of CNNs, however, is their lack of generalizability and tendency to overfit when presented with small training sets. Augmentation directly confronts this problem by generating new data points providing additional information. In this paper, we investigate the performance of more than ten different sets of data augmentation methods, with two novel approaches proposed here: one based on the discrete wavelet transform and the other on the constant-Q Gabor transform. Pretrained ResNet50 networks are finetuned on each augmentation method. Combinations of these networks are evaluated and compared across four benchmark data sets of images representing diverse problems and collected by instruments that capture information at different scales: a virus data set, a bark data set, a portrait dataset, and a LIGO glitches data set. Experiments demonstrate the superiority of this approach. The best ensemble proposed in this work achieves state-of-the-art (or comparable) performance across all four data sets. This result shows that varying data augmentation is a feasible way for building an ensemble of classifiers for image classification.<\/jats:p>","DOI":"10.3390\/jimaging7120254","type":"journal-article","created":{"date-parts":[[2021,11,30]],"date-time":"2021-11-30T23:22:28Z","timestamp":1638314548000},"page":"254","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":76,"title":["Comparison of Different Image Data Augmentation Approaches"],"prefix":"10.3390","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3502-7209","authenticated-orcid":false,"given":"Loris","family":"Nanni","sequence":"first","affiliation":[{"name":"Department of Information Engineering, University of Padua, Via Gradenigo 6, 35131 Padova, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0510-1444","authenticated-orcid":false,"given":"Michelangelo","family":"Paci","sequence":"additional","affiliation":[{"name":"BioMediTech, Faculty of Medicine and Health Technology, Tampere University, Arvo Ylp\u00f6n katu 34, FI-33520 Tampere, Finland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7664-6930","authenticated-orcid":false,"given":"Sheryl","family":"Brahnam","sequence":"additional","affiliation":[{"name":"Computer Information Systems, Missouri State University, 901 S. National, Springfield, MO 65804, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0290-7354","authenticated-orcid":false,"given":"Alessandra","family":"Lumini","sequence":"additional","affiliation":[{"name":"Dipartimento di Informatica\u2013Scienza e Ingegneria (DISI), Universit\u00e0 di Bologna, Via dell\u2019Universit\u00e0 50, 47521 Cesena, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,27]]},"reference":[{"key":"ref_1","first-page":"8","article-title":"Dataset Growth in Medical Image Analysis Research","volume":"7","author":"Landau","year":"2021","journal-title":"J. Imaging"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L., Li, K., and Fei-Fei, L. (2009, January 20\u201325). ImageNet: A large-scale hierarchical image database. 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