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PulmoNet:一种基于深度学习的肺部疾病检测模型

AbdulRahman Tosho Abdulahi¹, Roseline Oluwaseun Ogundokun^2,3, Ajiboye Raimot Adenike⁴

¹Department of Computer Science, Institute of Information and Communication Technology, Kwara State Polytechnic, Ilorin, Nigeria.

BMC medical imaging

|February 28, 2024

概括

这项研究引入了一种深度卷积神经网络模型,用于使用放射学检测COVID-19和肺炎等肺部疾病. 人工智能模型实现了高准确性,提供了成本效益高的诊断解决方案,特别适用于发展中国家.

科学领域:

医疗成像医学成像
人工智能的人工智能
肺部病理学肺部病理学

背景情况:

肺部疾病带来了重大的健康挑战,从普通感冒到危及生命的疾病,如肺炎和COVID-19.
肺部感染的准确及时诊断至关重要,但成本高昂,特别是在发展中国家.
放射学,包括X射线和CT扫描,有助于检测这些感染,推动了先进分析方法的需求.

研究的目的:

开发和评估一个深层卷积神经网络 (DCNN) 模型,用于检测三个不同的肺部疾病:COVID-19,细菌性肺炎 (BP) 和病毒性肺炎 (VP).
使用图像增强技术优化DCNN模型,以提高检测准确度.
为了评估模型的性能与传统的方法对肺部疾病识别从放射图像.

主要方法:

一个深层卷积神经网络 (DCNN) 模型被设计和实施用于基于图像的肺部疾病检测.
使用图像增强技术来优化DCNN模型的训练和性能.
该模型在一个包含10,325例健康病例,3,749例COVID-19病例,883例细菌性肺炎病例和1,478例病毒性肺炎病例的数据集上进行了训练和测试.

主要成果:

该DCNN模型展示了高的检测准确性,达到COVID-19的94%的平均值,细菌性肺炎的95.4%,病毒性肺炎的99.4%,整体98.30%.
该模型表现出高效的训练和检测时间,训练约60秒,检测约50秒.
拟议的DCNN方法与肺部疾病识别的传统纹理描述技术相比,显示出更高的性能.

结论:

开发的DCNN模型显示了从放射图像中准确有效地检测关键肺部疾病的巨大潜力.
这种人工智能驱动的方法为医学诊断提供了有希望的进步,有可能改善医疗保健的可访问性和结果,特别是在资源有限的环境中.
这项研究强调了深度学习在提升早期识别COVID-19和肺炎等关键呼吸系统疾病方面的有效性.

关键词:

准确度准确度准确度准确度准确度图像扫描 (CT) 扫描是一种扫描.深度卷积神经网络是一个深度卷积神经网络.机器学习是机器学习.肺部疾病肺部疾病这是X射线.

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