{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T05:50:46Z","timestamp":1791265846889,"version":"4.1.0"},"reference-count":41,"publisher":"Wiley","issue":"9","license":[{"start":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T00:00:00Z","timestamp":1788134400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T00:00:00Z","timestamp":1788134400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Cancer Medicine"],"published-print":{"date-parts":[[2026,9]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>\n                    White blood cell differential (WBC\u2010Diff) scattergrams are used to screen for hematological abnormalities in potential hematopoietic malignancies and abnormal conditions. However, these potential indications can be easily missed. We applied deep learning models to develop a new and rapid workflow by learning the characteristics of WBC\u2010Diff scattergram images for early hematological abnormality screening. A total of 4297 WBC\u2010Diff scattergram images, including healthy controls, were included and divided into 19 categories. Eleven models encompassing both CNNs (ResNet18, ResNet50, VGG16, InceptionV3, DenseNet121, MobileNetV2, EfficientNet\u2010B0, and EfficientNet\u2010B1) and Vision Transformers (Swin\u2010Tiny, DeiT\u2010Base, and ConvNeXt\u2010Tiny) were trained and compared. Grad\u2010CAM interpretability analysis, hierarchical clustering, and multivariate analysis of variance (MANOVA) were used to characterize the learned feature space. Among all models, Swin\u2010Tiny achieved the highest accuracy (70.44%), while EfficientNet\u2010B0 was selected as the primary model due to its favorable balance of classification performance (67.90% accuracy) and computational efficiency (4.0\u2009M parameters, 0.42\u2009G floating\u2010point operations (GFLOPs), 2.8\u2009\u00d7\u2009faster CPU inference than Swin\u2010Tiny). The 5\u2010run stability evaluation confirmed reproducible performance, and evaluation on an independent hold\u2010out dataset confirmed patient\u2010independent generalizability. As an upstream triage endpoint (normal vs. abnormal), the model achieved a sensitivity of 0.975 (test) and 0.984 (independent hold\u2010out set) with a false\u2010negative rate of 2.5% and 1.6%, respectively. At the screening level, only 2.2% of abnormal images were mis\u2010routed to \u201cnormal,\u201d reflecting a sensitivity\u2010first, specificity\u2010as\u2010safeguard design. Grad\u2010CAM analysis revealed disease\u2010specific activation patterns that mapped to established cell population zones on the WBC\u2010Diff scattergram (e.g., myeloblast zone for M1 and neutrophil\u2013basophil cluster for CML), providing support for the biological plausibility of model decisions, with significant differences among disease categories in the visual feature space (MANOVA,\n                    <jats:italic>p<\/jats:italic>\n                    \u2009&lt;\u20090.01). This study demonstrated that a new automated hematology workflow incorporating EfficientNet\u2010B0 could provide early and rapid screening of potential hematological abnormalities, particularly suitable for deployment in resource\u2010constrained clinical settings.\n                  <\/jats:p>","DOI":"10.1002\/cam4.72196","type":"journal-article","created":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T05:23:27Z","timestamp":1788240207000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["<scp>EfficientNet<\/scp>\n                    \u2010\n                    <scp>B0<\/scp>\n                    \u2013Based Screening of\n                    <scp>WBC<\/scp>\n                    \u2010Diff Scattergram Images for Hematological Abnormalities"],"prefix":"10.1002","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-6476-0425","authenticated-orcid":false,"given":"Mingzhu","family":"Long","sequence":"first","affiliation":[{"name":"The First Clinical College The First Affiliated Hospital of Chongqing Medical University  Chongqing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuan","family":"Luo","sequence":"additional","affiliation":[{"name":"Department of Clinical Laboratory The First Affiliated Hospital of Chongqing Medical University  Chongqing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lixia","family":"Zhang","sequence":"additional","affiliation":[{"name":"Hematology Department The First Affiliated Hospital of Chongqing Medical University  Chongqing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7153-4016","authenticated-orcid":false,"given":"Te","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Clinical Laboratory The First Affiliated Hospital of Chongqing Medical University  Chongqing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0228-5340","authenticated-orcid":false,"given":"Haixia","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Clinical Laboratory The First Affiliated Hospital of Chongqing Medical University  Chongqing 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