{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T18:10:25Z","timestamp":1778609425806,"version":"3.51.4"},"reference-count":32,"publisher":"Public Library of Science (PLoS)","issue":"5","license":[{"start":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T00:00:00Z","timestamp":1778544000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>\n                    Peripheral blood smear analysis continues to be important for the diagnosis of various hematologic conditions, although automated systems often experience difficulties due to the variety of cell types and the multi-class detection requirements associated with them. In this work, we propose Elastic YOLO (EYOLO), an extension of the You Only Look Once (YOLO) object detection framework designed for morphology-aware detection of red blood cells, white blood cells, and platelets in peripheral blood smear images. In a dataset annotated by clinicians, Elastic YOLO achieved a mAP@0.5 of 94.7% and mAP@0.5:0.95 of 87.8%, outperforming the baseline YOLOv5 model and several recent detection architectures while achieving inference speeds of up to 78 frames per second (FPS) under high-performance GPU settings (NVIDIA RTX 4090) at\n                    <jats:inline-formula id=\"pone.0347123.e001\">\n                      <jats:alternatives>\n                        <jats:graphic xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" id=\"pone.0347123.e001g\" mimetype=\"image\" position=\"anchor\" xlink:href=\"info:doi\/10.1371\/journal.pone.0347123.e001\" xlink:type=\"simple\"\/>\n                        <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" display=\"inline\" id=\"M1\">\n                          <mml:mrow>\n                            <mml:mn>256<\/mml:mn>\n                            <mml:mo>\u00d7<\/mml:mo>\n                            <mml:mn>256<\/mml:mn>\n                          <\/mml:mrow>\n                        <\/mml:math>\n                      <\/jats:alternatives>\n                    <\/jats:inline-formula>\n                    input resolution. Elastic adaptive convolutions form the core of the framework, allowing the receptive field to adapt dynamically to variations in cell size, shape, and staining conditions across different cell types. The automated smear image analysis offered by this system may assist computer-assisted hematology workflows and can support remote screening scenarios such as telemedicine-based triage, where rapid preliminary analysis of peripheral smear images is required.\n                  <\/jats:p>","DOI":"10.1371\/journal.pone.0347123","type":"journal-article","created":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T17:34:58Z","timestamp":1778607298000},"page":"e0347123","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Adaptive elastic convolution-based YOLO for peripheral blood smear cell detection"],"prefix":"10.1371","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-2719-7949","authenticated-orcid":true,"given":"Neha Margret","family":"Issac","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6614-8647","authenticated-orcid":true,"given":"Rajakumar","family":"K","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"340","published-online":{"date-parts":[[2026,5,12]]},"reference":[{"key":"pone.0347123.ref001","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.aej.2025.03.098","article-title":"White blood cells detection using deep learning in healthcare applications","volume":"124","author":"A Abozeid","year":"2025","journal-title":"Alexandria Engineering Journal"},{"issue":"2","key":"pone.0347123.ref002","article-title":"Using deep learning on microscopic images for white blood cell detection and segmentation to assist in leukemia diagnosis","volume":"81","author":"FRT Ferreira","year":"2025","journal-title":"J Supercomput"},{"issue":"30","key":"pone.0347123.ref003","doi-asserted-by":"crossref","first-page":"36963","DOI":"10.1007\/s11042-025-20644-1","article-title":"Red blood cell segmentation and classification using hybrid image processing-squeezenet model","volume":"84","author":"N Prakash","year":"2025","journal-title":"Multimed Tools Appl"},{"issue":"6","key":"pone.0347123.ref004","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0218808","article-title":"Deep learning approach to peripheral leukocyte recognition","volume":"14","author":"Q Wang","year":"2019","journal-title":"PLoS One"},{"issue":"18","key":"pone.0347123.ref005","doi-asserted-by":"crossref","first-page":"25541","DOI":"10.1007\/s11042-022-12209-3","article-title":"ErythroidCounter: an automatic pipeline for erythroid cell detection, identification and counting based on deep learning","volume":"81","author":"Y Zhou","year":"2022","journal-title":"Multimed Tools Appl"},{"key":"pone.0347123.ref006","doi-asserted-by":"crossref","first-page":"101761","DOI":"10.1016\/j.tice.2022.101761","article-title":"A comparative assessment of deep object detection models for blood smear analysis","volume":"76","author":"K Talukdar","year":"2022","journal-title":"Tissue Cell"},{"issue":"6","key":"pone.0347123.ref007","doi-asserted-by":"crossref","first-page":"4100","DOI":"10.1007\/s10278-025-01460-3","article-title":"SSW-YOLO: Enhanced Blood Cell Detection with Improved Feature Extraction and Multi-scale Attention","volume":"38","author":"H Sun","year":"2025","journal-title":"J Imaging Inform Med"},{"issue":"5","key":"pone.0347123.ref008","doi-asserted-by":"crossref","first-page":"1367","DOI":"10.1002\/jemt.24775","article-title":"Highly-Efficient Differentiation of Reactive Lymphocytes in Peripheral Blood Using Multi-Object Detection Network With Large Kernels","volume":"88","author":"Z Liu","year":"2025","journal-title":"Microsc Res Tech"},{"issue":"5","key":"pone.0347123.ref009","doi-asserted-by":"crossref","first-page":"404","DOI":"10.3390\/biomimetics8050404","article-title":"SDE-YOLO: A Novel Method for Blood Cell Detection","volume":"8","author":"Y Wu","year":"2023","journal-title":"Biomimetics (Basel)"},{"key":"pone.0347123.ref010","doi-asserted-by":"crossref","first-page":"102677","DOI":"10.1016\/j.tice.2024.102677","article-title":"Malaria parasite detection in Red Blood Cells with rouleaux formation morphology using YOLOv9","volume":"93","author":"FA Muhammad","year":"2025","journal-title":"Tissue Cell"},{"key":"pone.0347123.ref011","doi-asserted-by":"crossref","first-page":"100175","DOI":"10.1016\/j.ibmed.2024.100175","article-title":"A Convolutional Neural Network- Based Deep Learning To Detect Reticulocytes From Human Peripheral Blood","volume":"10","author":"K Reghunandanan","year":"2024","journal-title":"Intelligence-Based Medicine"},{"key":"pone.0347123.ref012","doi-asserted-by":"crossref","first-page":"107875","DOI":"10.1016\/j.compbiomed.2023.107875","article-title":"WBC YOLO-ViT: 2 Way - 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