{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T19:03:53Z","timestamp":1782846233656,"version":"3.54.5"},"reference-count":0,"publisher":"ECMS","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,6,23]]},"abstract":"<jats:p>Automated medical image analysis increasingly relies on models that are not only accurate but also robust, interpretable, and efficient enough for practical inference pipelines. This paper presents an end-to-end modelling and inference workflow for automated blood cell classification using convolutional neural networks trained on the BloodMNIST benchmark. We systematically compare classical machine learning baselines (logistic regression, SVM, random forests, MLP) with custom CNN architectures and a transfer-learning model (fine-tuned ResNet-18). On the BloodMNIST test set, the best model (fine-tuned ResNet-18) achieves 97.2\\% accuracy and a macro-averaged F1-score of 0.969, outperforming the strongest classical baseline (0.87 accuracy, 0.86 macro-F1). To support explainable modelling, Grad-CAM visualisations are used to highlight image regions driving predictions. Robustness is assessed by modelling typical microscopy acquisition variability (rotations, colour jitter, noise, blur), showing only moderate degradation under realistic perturbations. Finally, we demonstrate deployability as an end-to-end system aspect via a RESTful API and a lightweight GUI enabling real-time single-image inference on commodity hardware.<\/jats:p>","DOI":"10.7148\/2026-0660","type":"proceedings-article","created":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T18:08:43Z","timestamp":1782842923000},"page":"660-668","source":"Crossref","is-referenced-by-count":0,"title":["Automated blood cell classification using computer vision and convolutional neural networks"],"prefix":"10.7148","author":[{"given":"Maciej","family":"Labuz","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Filip","family":"Kruzel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"4144","published-online":{"date-parts":[[2026,6,23]]},"event":{"name":"40th ECMS International Conference on Modelling and Simulation"},"container-title":["ECMS 2026 Proceedings edited by Filippo Sanfilippo, Florenc Demrozi, Fabio Sgarbossa, Mohammad Poursina"],"original-title":[],"deposited":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T18:08:45Z","timestamp":1782842925000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.scs-europe.net\/dlib\/2026\/ecms2026acceptedpapers\/0660_dis_ecms2026_0043.pdf"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,23]]},"references-count":0,"URL":"https:\/\/doi.org\/10.7148\/2026-0660","relation":{},"subject":[],"published":{"date-parts":[[2026,6,23]]}}}