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轻质卷积神经网络用于胸部X射线图像的分类

Chih-Ta Yen¹, Chia-Yu Tsao²

¹Department of Electrical Engineering, National Taiwan Ocean University, Keelung City, 202301, Taiwan. chihtayen@gmail.com.

Scientific reports

|November 29, 2024

概括

一个新的轻量级卷积神经网络 (CNN) 从胸部X射线快速诊断COVID-19. 这种人工智能模型实现了高精度,帮助医疗专业人员快速有效地检测疾病.

科学领域:

医疗成像医学成像
人工智能的人工智能
计算机科学计算机科学

背景情况:

准确和快速诊断COVID-19对于有效的患者管理和疾病控制至关重要.
胸部X射线成像是诊断呼吸系统疾病的广泛可用和具有成本效益的工具.
医疗图像分析的现有深度学习模型可能是计算密集型,限制实时应用.

研究的目的:

开发一种轻量级和高效的卷积神经网络 (CNN) 架构,以从胸部X射线图像中快速检测COVID-19.
提高AI模型用于COVID-19诊断的速度和减少AI模型的计算要求.
用公开可用的COVID-19放射数据集验证拟议的CNN模型.

主要方法:

开发一种新的CNN架构,包括重新设计的特征提取 (FE) 模块和多尺度特征 (MF) 模块.
在COVID-19放射数据库的多个版本上对CNN模型的培训和验证.
评估模型在三个类别的表现:COVID-19,病毒性/细菌性肺炎和正常的胸部X射线图像.

主要成果:

拟议的CNN在三类分类任务中实现了99.85%的训练准确率和96.28%的验证准确率.
测试组的最佳准确率为COVID-19的96.03%,病毒性/细菌性肺炎的97.10%,正常病例的97.86%.
该模型显示,与现有方法相比,计算要求降低,处理速度提高.

结论:

开发的轻量级CNN为COVID-19诊断提供了使用胸部X射线的快速和准确的解决方案.
拟议的架构有效地提取了区分COVID-19,肺炎和正常病例的相关特征.
这种人工智能工具有可能支持医疗专业人员实时进行COVID-19检测的临床决策.

关键词:

在 COVID-19 疫情中,胸部X射线成像胸部X射线成像计算机辅助诊断是一种计算机辅助的诊断.卷积神经网络是一种卷积神经网络.轻量级的建筑轻量级的建筑.

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