{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T14:26:03Z","timestamp":1771079163222,"version":"3.50.1"},"reference-count":48,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2022,10,21]],"date-time":"2022-10-21T00:00:00Z","timestamp":1666310400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"UCF College of Graduate Studies Open Access Publishing Fund"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>Accurate and robust human immune system assessment through white blood cell evaluation require computer-aided tools with pathologist-level accuracy. This work presents a multi-attention leukocytes subtype classification method by leveraging fine-grained and spatial locality attributes of white blood cell. The proposed framework comprises three main components: texture-aware\/attention map generation blocks, attention regularization, and attention-based data augmentation. The developed framework is applicable to general CNN-based architectures and enhances decision making by paying specific attention to the discriminative regions of a white blood cell. The performance of the proposed method\/model was evaluated through an extensive set of experiments and validation. The obtained results demonstrate the superior performance of the model achieving 99.69 % accuracy compared to other state-of-the-art approaches. The proposed model is a good alternative and complementary to existing computer diagnosis tools to assist pathologists in evaluating white blood cells from blood smear images.<\/jats:p>","DOI":"10.3390\/bdcc6040122","type":"journal-article","created":{"date-parts":[[2022,10,23]],"date-time":"2022-10-23T20:43:50Z","timestamp":1666557830000},"page":"122","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["White Blood Cell Classification Using Multi-Attention Data Augmentation and Regularization"],"prefix":"10.3390","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4184-2920","authenticated-orcid":false,"given":"Nasrin","family":"Bayat","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Central Florida, Orlando, FL 32816, USA"}]},{"given":"Diane D.","family":"Davey","sequence":"additional","affiliation":[{"name":"College of Medicine, University of Central Florida, 6850 Lake Nona Blvd, Orlando, FL 32827, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4543-4756","authenticated-orcid":false,"given":"Melanie","family":"Coathup","sequence":"additional","affiliation":[{"name":"College of Medicine, University of Central Florida, 6850 Lake Nona Blvd, Orlando, FL 32827, USA"}]},{"given":"Joon-Hyuk","family":"Park","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Central Florida, Orlando, FL 32816, USA"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,21]]},"reference":[{"key":"ref_1","first-page":"71","article-title":"Peripheral blood film-a review","volume":"12","author":"Adewoyin","year":"2014","journal-title":"Ann. 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