Automated Blood Cell Detection and Classification in Microscopic Images Using YOLOv11 and Optimized Weights

<b>Background/Objectives:</b> Accurate detection and classification of blood cell types in microscopic images are crucial for diagnosing various hematological conditions. This study aims to develop and evaluate advanced architectures for automating blood cell detection and classification...

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Main Authors: Halenur Sazak, Muhammed Kotan
Format: Article
Language:English
Published: MDPI AG 2024-12-01
Series:Diagnostics
Subjects:
Online Access:https://www.mdpi.com/2075-4418/15/1/22
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author Halenur Sazak
Muhammed Kotan
author_facet Halenur Sazak
Muhammed Kotan
author_sort Halenur Sazak
collection DOAJ
description <b>Background/Objectives:</b> Accurate detection and classification of blood cell types in microscopic images are crucial for diagnosing various hematological conditions. This study aims to develop and evaluate advanced architectures for automating blood cell detection and classification using the newly proposed YOLOv10 and YOLOv11 models, with a specific focus on identifying red blood cells (RBCs), white blood cells (WBCs), and platelets in microscopic images as a preliminary step of the complete blood count (CBC). <b>Methods</b>: The Blood Cell Count Detection (BCCD) dataset was enriched using data augmentation techniques to improve model robustness and diversity. Extensive experiments were performed, including complete weight initialization, advanced optimization strategies, and meticulous hyperparameter tuning for the YOLOv11 architecture. <b>Results</b>: The YOLOv11-l model achieved an overall mean Average Precision (mAP) of 93.8%, reflecting its robust accuracy across multiple blood cell types. <b>Conclusions</b>: The findings underscore the efficacy of the YOLOv11 architecture in automating blood cell classification with high precision, demonstrating its potential to enhance hematological analyses and support clinical diagnosis.
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spelling doaj-art-7726535942e54f35b708056d60d6e4462025-01-10T13:16:29ZengMDPI AGDiagnostics2075-44182024-12-011512210.3390/diagnostics15010022Automated Blood Cell Detection and Classification in Microscopic Images Using YOLOv11 and Optimized WeightsHalenur Sazak0Muhammed Kotan1Department of Information Systems Engineering, Faculty of Computer and Information Sciences, Sakarya University, Sakarya 54050, TurkeyDepartment of Information Systems Engineering, Faculty of Computer and Information Sciences, Sakarya University, Sakarya 54050, Turkey<b>Background/Objectives:</b> Accurate detection and classification of blood cell types in microscopic images are crucial for diagnosing various hematological conditions. This study aims to develop and evaluate advanced architectures for automating blood cell detection and classification using the newly proposed YOLOv10 and YOLOv11 models, with a specific focus on identifying red blood cells (RBCs), white blood cells (WBCs), and platelets in microscopic images as a preliminary step of the complete blood count (CBC). <b>Methods</b>: The Blood Cell Count Detection (BCCD) dataset was enriched using data augmentation techniques to improve model robustness and diversity. Extensive experiments were performed, including complete weight initialization, advanced optimization strategies, and meticulous hyperparameter tuning for the YOLOv11 architecture. <b>Results</b>: The YOLOv11-l model achieved an overall mean Average Precision (mAP) of 93.8%, reflecting its robust accuracy across multiple blood cell types. <b>Conclusions</b>: The findings underscore the efficacy of the YOLOv11 architecture in automating blood cell classification with high precision, demonstrating its potential to enhance hematological analyses and support clinical diagnosis.https://www.mdpi.com/2075-4418/15/1/22YOLOv11blood cell detectionautomated detectionmedical imagingcomputer vision
spellingShingle Halenur Sazak
Muhammed Kotan
Automated Blood Cell Detection and Classification in Microscopic Images Using YOLOv11 and Optimized Weights
Diagnostics
YOLOv11
blood cell detection
automated detection
medical imaging
computer vision
title Automated Blood Cell Detection and Classification in Microscopic Images Using YOLOv11 and Optimized Weights
title_full Automated Blood Cell Detection and Classification in Microscopic Images Using YOLOv11 and Optimized Weights
title_fullStr Automated Blood Cell Detection and Classification in Microscopic Images Using YOLOv11 and Optimized Weights
title_full_unstemmed Automated Blood Cell Detection and Classification in Microscopic Images Using YOLOv11 and Optimized Weights
title_short Automated Blood Cell Detection and Classification in Microscopic Images Using YOLOv11 and Optimized Weights
title_sort automated blood cell detection and classification in microscopic images using yolov11 and optimized weights
topic YOLOv11
blood cell detection
automated detection
medical imaging
computer vision
url https://www.mdpi.com/2075-4418/15/1/22
work_keys_str_mv AT halenursazak automatedbloodcelldetectionandclassificationinmicroscopicimagesusingyolov11andoptimizedweights
AT muhammedkotan automatedbloodcelldetectionandclassificationinmicroscopicimagesusingyolov11andoptimizedweights