AI-Assisted Web Application for Leukocyte Abnormality Counting With YOLOv11 and Smartphone Microscopy
Accurate and timely white blood cell (WBC) analysis is crucial for diagnosing hematological disorders, often requiring microscopic examination of peripheral blood smears (PBS). While manual counting by trained specialists is considered the gold standard, it is time-consuming and impractical in resou...
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IEEE
2025-01-01
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| author | Natthakorn Kasamsumran Piyalitt Ittichaiwong Chutinart Chinudomporn Kanyakorn Veerakanjana Ekapun Karoopongse Wanchalerm Pora |
| author_facet | Natthakorn Kasamsumran Piyalitt Ittichaiwong Chutinart Chinudomporn Kanyakorn Veerakanjana Ekapun Karoopongse Wanchalerm Pora |
| author_sort | Natthakorn Kasamsumran |
| collection | DOAJ |
| description | Accurate and timely white blood cell (WBC) analysis is crucial for diagnosing hematological disorders, often requiring microscopic examination of peripheral blood smears (PBS). While manual counting by trained specialists is considered the gold standard, it is time-consuming and impractical in resource-limited settings. Automated cell counters can misclassify similitude or immature cells, hindering accurate diagnosis. To address these limitations, we propose an AI-powered web application that utilizes YOLOv11 with enhanced small object detection capabilities, enabled by integrating our C3k2-Conv blocks, an architecture inspired by C3k2. Our model, trained on eleven WBC classes and nucleated red blood cells (NRBCs), achieves an impressive mean average precision (mAP@0.5) of approximately 0.9000 on validation and unseen test sets, demonstrating a performance comparable to human specialists in identifying and quantifying WBCs. Furthermore, our research demonstrates that providing general practitioners and medical students with PBS images annotated by our AI model significantly improves their counting accuracy and reduces the time spent on manual counting. Our web application, Myelosoft, allows clinicians to upload smartphone-captured PBS images for rapid and automated analysis. The system provides comprehensive differential counts for 11 WBC classes, including atypical lymphocytes, band neutrophils, basophils, blasts, eosinophils, lymphocytes, metamyelocytes, monocytes, myelocytes, promyelocytes, and segmented neutrophils, as well as NRBCs. This real-time analysis facilitates timely diagnosis and treatment, potentially reducing risks associated with delayed interventions. Our approach offers a robust and accessible solution for improving hematologic treatment, especially in resource-constrained environments. |
| format | Article |
| id | doaj-art-1ef47e785af34f08a4359bb5759276d2 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-1ef47e785af34f08a4359bb5759276d22025-08-20T02:29:42ZengIEEEIEEE Access2169-35362025-01-0113890798910710.1109/ACCESS.2025.356976711003063AI-Assisted Web Application for Leukocyte Abnormality Counting With YOLOv11 and Smartphone MicroscopyNatthakorn Kasamsumran0https://orcid.org/0000-0001-7470-1633Piyalitt Ittichaiwong1https://orcid.org/0000-0002-4950-7764Chutinart Chinudomporn2https://orcid.org/0009-0007-8172-1350Kanyakorn Veerakanjana3https://orcid.org/0000-0002-7549-8865Ekapun Karoopongse4Wanchalerm Pora5https://orcid.org/0000-0002-1170-2177Department of Electrical Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, ThailandSiriraj Informatics and Data Innovation Center, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, ThailandFaculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, ThailandSiriraj Informatics and Data Innovation Center, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, ThailandDepartment of Internal Medicine, Division of Hematology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, ThailandDepartment of Electrical Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, ThailandAccurate and timely white blood cell (WBC) analysis is crucial for diagnosing hematological disorders, often requiring microscopic examination of peripheral blood smears (PBS). While manual counting by trained specialists is considered the gold standard, it is time-consuming and impractical in resource-limited settings. Automated cell counters can misclassify similitude or immature cells, hindering accurate diagnosis. To address these limitations, we propose an AI-powered web application that utilizes YOLOv11 with enhanced small object detection capabilities, enabled by integrating our C3k2-Conv blocks, an architecture inspired by C3k2. Our model, trained on eleven WBC classes and nucleated red blood cells (NRBCs), achieves an impressive mean average precision (mAP@0.5) of approximately 0.9000 on validation and unseen test sets, demonstrating a performance comparable to human specialists in identifying and quantifying WBCs. Furthermore, our research demonstrates that providing general practitioners and medical students with PBS images annotated by our AI model significantly improves their counting accuracy and reduces the time spent on manual counting. Our web application, Myelosoft, allows clinicians to upload smartphone-captured PBS images for rapid and automated analysis. The system provides comprehensive differential counts for 11 WBC classes, including atypical lymphocytes, band neutrophils, basophils, blasts, eosinophils, lymphocytes, metamyelocytes, monocytes, myelocytes, promyelocytes, and segmented neutrophils, as well as NRBCs. This real-time analysis facilitates timely diagnosis and treatment, potentially reducing risks associated with delayed interventions. Our approach offers a robust and accessible solution for improving hematologic treatment, especially in resource-constrained environments.https://ieeexplore.ieee.org/document/11003063/Object detectionleukocyte detectionmicroscopy imagingperipheral blood smearwhite blood cellweb application |
| spellingShingle | Natthakorn Kasamsumran Piyalitt Ittichaiwong Chutinart Chinudomporn Kanyakorn Veerakanjana Ekapun Karoopongse Wanchalerm Pora AI-Assisted Web Application for Leukocyte Abnormality Counting With YOLOv11 and Smartphone Microscopy IEEE Access Object detection leukocyte detection microscopy imaging peripheral blood smear white blood cell web application |
| title | AI-Assisted Web Application for Leukocyte Abnormality Counting With YOLOv11 and Smartphone Microscopy |
| title_full | AI-Assisted Web Application for Leukocyte Abnormality Counting With YOLOv11 and Smartphone Microscopy |
| title_fullStr | AI-Assisted Web Application for Leukocyte Abnormality Counting With YOLOv11 and Smartphone Microscopy |
| title_full_unstemmed | AI-Assisted Web Application for Leukocyte Abnormality Counting With YOLOv11 and Smartphone Microscopy |
| title_short | AI-Assisted Web Application for Leukocyte Abnormality Counting With YOLOv11 and Smartphone Microscopy |
| title_sort | ai assisted web application for leukocyte abnormality counting with yolov11 and smartphone microscopy |
| topic | Object detection leukocyte detection microscopy imaging peripheral blood smear white blood cell web application |
| url | https://ieeexplore.ieee.org/document/11003063/ |
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