Advancing Leukocyte Classification: A Cutting-Edge Deep Learning Approach for AI-Driven Clinical Diagnosis

IF 3 4区计算机科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

International Journal of Imaging Systems and Technology Pub Date : 2024-10-28 DOI:10.1002/ima.23204

Ahmadsaidulu Shaik, Abhishek Tiwari, Balachakravarthy Neelapu, Puneet Kumar Jain, Earu Banoth

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Abstract

White blood cells (WBCs) are crucial components of the immune system, responsible for detecting and eliminating pathogens. Accurate detection and classification of WBCs are essential for various clinical diagnostics. This study aims to develop an AI framework for detecting and classifying WBCs from microscopic images using a customized YOLOv5 model with three key modifications. Firstly, the C3 module in YOLOv5's backbone is replaced with the innovative C3TR structure to enhance feature extraction and reduce background noise. Secondly, the BiFPN is integrated into the neck to improve feature localization and discrimination. Thirdly, an additional layer in the head enhances detection of small WBCs. Experiments on the BCCD dataset, comprising 352 microscopic blood smear images with leukocytes, demonstrated the framework's superiority over state-of-the-art methods, achieving 99.4% accuracy. Furthermore, the model exhibits computational efficiency, operating over five times faster than existing YOLO models. These findings underscore the framework's promise in medical diagnostics, showcasing deep learning's supremacy in automated cell classification.

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推进白细胞分类：用于人工智能临床诊断的前沿深度学习方法

白细胞（WBC）是免疫系统的重要组成部分，负责检测和消除病原体。白细胞的准确检测和分类对于各种临床诊断至关重要。本研究旨在开发一个人工智能框架，利用定制的 YOLOv5 模型，通过三处关键修改，从显微图像中检测白细胞并对其进行分类。首先，用创新的 C3TR 结构取代了 YOLOv5 主干网中的 C3 模块，以增强特征提取并减少背景噪音。其次，将 BiFPN 集成到颈部，以提高特征定位和辨别能力。第三，在头部增加了一层，增强了对小白细胞的检测。BCCD 数据集包括 352 幅带有白细胞的显微血液涂片图像，在该数据集上进行的实验表明，该框架优于最先进的方法，准确率达到 99.4%。此外，该模型的计算效率也很高，比现有的 YOLO 模型快五倍以上。这些发现强调了该框架在医疗诊断方面的前景，展示了深度学习在自动细胞分类方面的优势。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Imaging Systems and Technology 工程技术-成像科学与照相技术

CiteScore

6.90

自引率

6.10%

发文量

138

审稿时长

3 months

期刊介绍： The International Journal of Imaging Systems and Technology (IMA) is a forum for the exchange of ideas and results relevant to imaging systems, including imaging physics and informatics. The journal covers all imaging modalities in humans and animals. IMA accepts technically sound and scientifically rigorous research in the interdisciplinary field of imaging, including relevant algorithmic research and hardware and software development, and their applications relevant to medical research. The journal provides a platform to publish original research in structural and functional imaging. The journal is also open to imaging studies of the human body and on animals that describe novel diagnostic imaging and analyses methods. Technical, theoretical, and clinical research in both normal and clinical populations is encouraged. Submissions describing methods, software, databases, replication studies as well as negative results are also considered. The scope of the journal includes, but is not limited to, the following in the context of biomedical research: Imaging and neuro-imaging modalities: structural MRI, functional MRI, PET, SPECT, CT, ultrasound, EEG, MEG, NIRS etc.; Neuromodulation and brain stimulation techniques such as TMS and tDCS; Software and hardware for imaging, especially related to human and animal health; Image segmentation in normal and clinical populations; Pattern analysis and classification using machine learning techniques; Computational modeling and analysis; Brain connectivity and connectomics; Systems-level characterization of brain function; Neural networks and neurorobotics; Computer vision, based on human/animal physiology; Brain-computer interface (BCI) technology; Big data, databasing and data mining.