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This study presents an automated high\u2010throughput optical digital microscope system capable of sequentially scanning and analyzing 10 blood smear slides per batch in under 15\u2009min\u00a0using a Laplacian\u2010based autofocusing algorithm at 40x magnification. The acquired images are segmented via the YOLOv5 algorithm, and morphological features of red blood cells (RBCs) are classified using a multilayer perceptron (MLP) model. The system achieved 90.6% accuracy, 95% precision, 91% sensitivity, and 94% specificity in classifying anemia subtypes (macrocytic, microcytic, normocytic) and healthy samples. The trained model is integrated into an Android application for real\u2010time geographic mapping of anemic clusters, enabling healthcare workers to prioritize interventions efficiently. This high\u2010throughput approach eliminates the need for immersion oil and manual slide handling, demonstrating significant potential for rapid, scalable anemia screening in resource\u2010limited settings.<\/jats:p>","DOI":"10.1002\/jbio.70024","type":"journal-article","created":{"date-parts":[[2025,4,6]],"date-time":"2025-04-06T20:15:39Z","timestamp":1743970539000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Development of a High\u2010Throughput Microscope for the Analysis of Peripheral Blood Smears for Anemia Screening"],"prefix":"10.1002","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7258-5293","authenticated-orcid":false,"given":"A. 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