{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T09:13:12Z","timestamp":1772701992769,"version":"3.50.1"},"update-to":[{"DOI":"10.1371\/journal.pcbi.1012327","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2024,8,15]],"date-time":"2024-08-15T00:00:00Z","timestamp":1723680000000}}],"reference-count":28,"publisher":"Public Library of Science (PLoS)","issue":"8","license":[{"start":{"date-parts":[[2024,8,5]],"date-time":"2024-08-05T00:00:00Z","timestamp":1722816000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p><jats:italic>Plasmodium<\/jats:italic> parasites cause Malaria disease, which remains a significant threat to global health, affecting 200 million people and causing 400,000 deaths yearly. <jats:italic>Plasmodium falciparum<\/jats:italic> and <jats:italic>Plasmodium vivax<\/jats:italic> remain the two main malaria species affecting humans. Identifying the malaria disease in blood smears requires years of expertise, even for highly trained specialists. Literature studies have been coping with the automatic identification and classification of malaria. However, several points must be addressed and investigated so these automatic methods can be used clinically in a Computer-aided Diagnosis (CAD) scenario. In this work, we assess the transfer learning approach by using well-known pre-trained deep learning architectures. We considered a database with 6222 Region of Interest (ROI), of which 6002 are from the Broad Bioimage Benchmark Collection (BBBC), and 220 were acquired locally by us at Funda\u00e7\u00e3o Oswaldo Cruz (FIOCRUZ) in Porto Velho Velho, Rond\u00f4nia\u2014Brazil, which is part of the legal Amazon. We exhaustively cross-validated the dataset using 100 distinct partitions with 80% train and 20% test for each considering circular ROIs (rough segmentation). Our experimental results show that DenseNet201 has a potential to identify <jats:italic>Plasmodium<\/jats:italic> parasites in ROIs (infected or uninfected) of microscopic images, achieving 99.41% AUC with a fast processing time. We further validated our results, showing that DenseNet201 was significantly better (99% confidence interval) than the other networks considered in the experiment. Our results support claiming that transfer learning with texture features potentially differentiates subjects with malaria, spotting those with <jats:italic>Plasmodium<\/jats:italic> even in Leukocytes images, which is a challenge. In Future work, we intend scale our approach by adding more data and developing a friendly user interface for CAD use. We aim at aiding the worldwide population and our local natives living nearby the legal Amazon\u2019s rivers.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1012327","type":"journal-article","created":{"date-parts":[[2024,8,5]],"date-time":"2024-08-05T17:35:39Z","timestamp":1722879339000},"page":"e1012327","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":6,"title":["A transfer learning approach to identify Plasmodium in microscopic images"],"prefix":"10.1371","volume":"20","author":[{"given":"Jonathan da Silva","family":"Ramos","sequence":"first","affiliation":[]},{"given":"Ivo Henrique Provensi","family":"Vieira","sequence":"additional","affiliation":[]},{"given":"Wan Song","family":"Rocha","sequence":"additional","affiliation":[]},{"given":"Rosimar Pires","family":"Esquerdo","sequence":"additional","affiliation":[]},{"given":"Carolina Yukari Veludo","family":"Watanabe","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3386-0069","authenticated-orcid":true,"given":"Fernando Berton","family":"Zanchi","sequence":"additional","affiliation":[]}],"member":"340","published-online":{"date-parts":[[2024,8,5]]},"reference":[{"issue":"3","key":"pcbi.1012327.ref001","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1016\/j.cell.2016.07.055","article-title":"Malaria: Biology and Disease","volume":"167","author":"AF Cowman","year":"2016","journal-title":"Cell"},{"key":"pcbi.1012327.ref002","first-page":"1","article-title":"Overdiagnosis and Overtreatment of Malaria in Children That Presented with Fever in Lagos, Nigeria","volume":"2013","author":"OO Oladosu","year":"2013","journal-title":"International Scholarly Research Notices"},{"issue":"1","key":"pcbi.1012327.ref003","doi-asserted-by":"crossref","first-page":"e1900138","DOI":"10.1002\/bies.201900138","article-title":"Diagnosis of malaria parasites Plasmodium spp. In endemic areas: Current strategies for an ancient disease","volume":"42","author":"B Gitta","year":"2020","journal-title":"Bioessays"},{"issue":"1","key":"pcbi.1012327.ref004","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1186\/1475-2875-6-74","article-title":"An exploratory study of factors that affect the performance and usage of rapid diagnostic tests for malaria in the Limpopo Province, South Africa","volume":"6","author":"D Moonasar","year":"2007","journal-title":"Malaria Journal"},{"issue":"6 Suppl","key":"pcbi.1012327.ref005","doi-asserted-by":"crossref","first-page":"119","DOI":"10.4269\/ajtmh.2007.77.119","article-title":"A review of malaria diagnostic tools: microscopy and rapid diagnostic test (RDT)","volume":"77","author":"C Wongsrichanalai","year":"2007","journal-title":"Am J Trop Med Hyg"},{"key":"pcbi.1012327.ref006","doi-asserted-by":"crossref","first-page":"397","DOI":"10.3389\/fimmu.2019.00397","article-title":"-Specific CD8+ T Cell Epitopes","volume":"10","author":"J Heide","year":"2019","journal-title":"Front Immunol"},{"issue":"1","key":"pcbi.1012327.ref007","doi-asserted-by":"crossref","DOI":"10.1186\/s12936-019-2998-1","article-title":"Evaluation of malaria microscopy diagnostic performance at private health facilities in Tanzania","volume":"18","author":"B Ngasala","year":"2019","journal-title":"Malaria Journal"},{"issue":"2","key":"pcbi.1012327.ref008","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1002\/app5.315","article-title":"Addressing hard-to-reach populations for achieving malaria elimination in the Asia Pacific Malaria Elimination Network countries","volume":"8","author":"K Wangdi","year":"2021","journal-title":"Asia; 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