相关实验视频

Updated: May 29, 2025

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

Published on: November 19, 2018

基于CNN的两阶段白细胞分类框架

Siraj Khan¹, Muhammad Sajjad¹, José Escorcia-Gutierrez²

¹Digital Image Processing Laboratory (DIP Lab), Department of Computer Science, Islamia College University, 25120, Peshawar, Pakistan.

Computers in biology and medicine

|February 6, 2025

概括

这项研究引入了一个两阶段的CNN框架,用于准确的白细胞 (WBC) 分段和分类. 该方法提高了血液分析的诊断准确性,改善了患者的治疗结果.

科学领域:

医疗成像医学成像
计算生物学计算生物学
医疗保健中的人工智能

背景情况:

白细胞 (白细胞) 是各种疾病的关键诊断标记物.
从外围血液涂抹中手动计数白细胞是劳动密集型的,容易出现错误.
在显微镜图像中精确细分和分类白细胞对于可靠的诊断至关重要.

研究的目的:

开发一个强大的自动化系统,用于精确的白细胞细分和分类.
为了提高血液显微镜图像分析的准确性和效率.
通过先进的AI技术来提高诊断性能和患者的结果.

主要方法:

提出了一个两阶段卷积神经网络 (CNN) 框架.
YOLOv8m-seg被用于精确细分白细胞 (WBCs).
使用MobileNetV3将细分的白细胞细胞分为五种类型:淋巴细胞,单细胞,基细胞,乙细胞和中性细胞.

主要成果:

拟议的框架在多个数据集 (Raabin-WBC,PBC,LISC) 中实现了细分 (高达99.56%) 和分类 (高达99.63%) 的高精度.
与现有的最先进的方法相比,该系统表现出卓越的性能.
自动化方法显著提高了白细胞分析的准确性和效率.

结论:

开发的两阶段CNN框架为自动白细胞细分和分类提供了高度准确和高效的解决方案.
这种人工智能驱动的方法有可能显著提升血液学中的诊断能力.
白细胞分类的提高准确性有望改善患者的诊断和治疗结果.

关键词:

血液涂抹图像血液涂抹图像深度学习是一种深度学习.图像的分类图像的分类.图像细分图像细分图像细分移动网络V3 移动网络V3 白细胞是白细胞的组成部分.这就是YOLOv8的意义.

更多相关视频

Characterization of Human Monocyte Subsets by Whole Blood Flow Cytometry Analysis

Characterization of Human Monocyte Subsets by Whole Blood Flow Cytometry Analysis

Published on: October 17, 2018

Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging

Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging

Published on: October 4, 2024

相关概念视频

Classification of Leukocytes

Classification of Leukocytes

Leukocytes are classified into two groups based on the presence or absence of cytoplasmic granules. Granular leukocytes, which contain granules, belong to the myeloid lineage and are divided into three subtypes: neutrophils, eosinophils, and basophils. These cells are roughly spherical and characterized by the granules in their cytoplasm.
Neutrophils are the most abundant type of granular leukocytes, comprising 50-70% of all leukocytes. They feature small, evenly distributed granules and a...

Flow Cytometry

Flow Cytometry

The development of flow cytometry techniques began in 1934 with initial attempts by Andrew Moldavan, a bacteriologist who counted the cells in a flowing capillary system. Moldavan pumped cells through a capillary tube focused under a microscope for visualization. The invention of photometry allowed the measurement of differentially-stained cells, and Louis Kamentsky developed the first multiparameter flow cytometer in 1965 to identify and count the cancer cells in cervical tissue specimens.
In...