Two-stage CNN-based framework for leukocytes classification

被引：0

作者：

Khan, Siraj ^{[1
]}

Sajjad, Muhammad ^{[1
]}

Escorcia-Gutierrez, José ^{[2
]}

Dhahbi, Sami ^{[3
]}

Hijji, Mohammad ^{[4
]}

Muhammad, Khan ^{[5
]}

机构：

[1] Digital Image Processing Laboratory (DIP Lab), Department of Computer Science, Islamia College University, Peshawar

[2] Department of Computational Science and Electronics, Universidad de la Costa, CUC, Barranquilla

[3] Applied College of Mahail Aseer, King Khalid University, Muhayil Aseer

[4] Faculty of Computers and Information Technology, University of Tabuk, Tabuk

[5] Visual Analytics for Knowledge Laboratory (VIS2KNOW Lab), Department of Applied AI, School of Convergence, College of Computing and Informatics, Sungkyunkwan University, Seoul

来源：

Computers in Biology and Medicine | 2025年 / 187卷

基金：

新加坡国家研究基金会;

关键词：

Blood smear images; Deep learning; Image classification; Image segmentation; MobileNetV3; White blood cells; YOLOv8;

D O I：

10.1016/j.compbiomed.2024.109616

中图分类号：

学科分类号：

摘要：

Leukocytes are pivotal markers in health, crucial for diagnosing diseases like malaria and viral infections. Peripheral blood smear tests provide pathologists with vital insights into various medical conditions. Manual leukocyte counting is challenging and error-prone due to their complex structure. Accurate segmentation and classification of leukocytes remain challenging, impacting both accuracy and efficiency in blood microscopic image analysis. To overcome these limitations, we propose a robust two-stage CNN framework that integrates YOLOv8 for precise segmentation and MobileNetV3 for effective classification. Initially, WBCs are segmented using YOLOv8m-seg, extracting ROIs for subsequent analysis. Then, features from segmented ROIs are used to train MobileNetV3, classifying WBCs into lymphocytes, monocytes, basophils, eosinophils, and neutrophils. This framework significantly advances leukocyte categorization, enhancing diagnostic performance and patient outcomes. The proposed technique achieved impressive accuracy rates of 99.56 %, 99.19 % and 98.89 % during segmentation and 99.28 %, 99.63 % and 98.49 % during classification on Raabin-WBC, PBC and LISC datasets, respectively, outperforming state-of-the-art methods. © 2024 Elsevier Ltd

引用

共 75 条

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