{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T13:07:48Z","timestamp":1787663268788,"version":"build-2736575974"},"reference-count":49,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2023,12,20]],"date-time":"2023-12-20T00:00:00Z","timestamp":1703030400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Cutaneous leishmaniasis (CL) is a common illness that causes skin lesions, principally ulcerations, on exposed regions of the body. Although neglected tropical diseases (NTDs) are typically found in tropical areas, they have recently become more common along Africa\u2019s northern coast, particularly in Libya. The devastation of healthcare infrastructure during the 2011 war and the following conflicts, as well as governmental apathy, may be causal factors associated with this catastrophic event. The main objective of this study is to evaluate alternative diagnostic strategies for recognizing amastigotes of cutaneous leishmaniasis parasites at various stages using Convolutional Neural Networks (CNNs). The research is additionally aimed at testing different classification models employing a dataset of ultra-thin skin smear images of Leishmania parasite-infected people with cutaneous leishmaniasis. The pre-trained deep learning models including EfficientNetB0, DenseNet201, ResNet101, MobileNetv2, and Xception are used for the cutaneous leishmania parasite diagnosis task. To assess the models\u2019 effectiveness, we employed a five-fold cross-validation approach to guarantee the consistency of the models\u2019 outputs when applied to different portions of the full dataset. Following a thorough assessment and contrast of the various models, DenseNet-201 proved to be the most suitable choice. It attained a mean accuracy of 0.9914 along with outstanding results for sensitivity, specificity, positive predictive value, negative predictive value, F1-score, Matthew\u2019s correlation coefficient, and Cohen\u2019s Kappa coefficient. The DenseNet-201 model surpassed the other models based on a comprehensive evaluation of these key classification performance metrics.<\/jats:p>","DOI":"10.3390\/diagnostics14010012","type":"journal-article","created":{"date-parts":[[2023,12,20]],"date-time":"2023-12-20T11:24:33Z","timestamp":1703071473000},"page":"12","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Assessment of Deep Learning Models for Cutaneous Leishmania Parasite Diagnosis Using Microscopic Images"],"prefix":"10.3390","volume":"14","author":[{"given":"Ali Mansour","family":"Abdelmula","sequence":"first","affiliation":[{"name":"Department of Microbiology and Clinical Microbiology, Faculty of Medicine, Near East University, North Cyprus, Mersin 10, Lefko\u015fa 99010, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2248-5996","authenticated-orcid":false,"given":"Omid","family":"Mirzaei","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Faculty of Engineering, Near East University, North Cyprus, Mersin 10, Lefko\u015fa 99010, Turkey"},{"name":"Research Center for Science, Technology and Engineering (BILTEM), Near East University, TRNC, Mersin 10, Lefko\u015fa 99138, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1635-0051","authenticated-orcid":false,"given":"Emrah","family":"G\u00fcler","sequence":"additional","affiliation":[{"name":"Department of Molecular Biology and Genetics, Faculty of Arts and Sciences, European University of Lefke, Lefke 99010, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kaya","family":"S\u00fcer","sequence":"additional","affiliation":[{"name":"Department of Clinical Microbiology and Infectious Diseases, Faculty of Medicine, Near East University, North Cyprus, Mersin 10, Lefko\u015fa 99010, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,20]]},"reference":[{"key":"ref_1","first-page":"68","article-title":"A Stroll through the History of Monoxenous Trypanosomatids Infection in Vertebrate Hosts","volume":"12","author":"Boucinha","year":"2022","journal-title":"Front. 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