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挑战问题和深度学习技术的未来建议用于使用X射线和CT图像检测SARS-CoV-2:全面审查

Md Shofiqul Islam^1,2, Fahmid Al Farid³, F M Javed Mehedi Shamrat⁴

¹Computer Science and Engineering (CSE), Military Institute of Science and Technology (MIST), Dhaka, Bangladesh.

PeerJ. Computer science

|February 3, 2025

概括

深度学习 (DL) 模型为从医学图像中诊断SARS-CoV-2提供了一个有希望的解决方案. 移动NetV3模型在检测SARS-CoV-2中实现了98.11%的准确性,超过了其他DL技术.

科学领域:

医学成像分析医学成像分析
医疗保健中的人工智能
深度学习应用程序

背景情况:

准确的SARS-CoV-2诊断至关重要,但传统的成像方法 (CT,X射线) 是具有挑战性和耗时的.
人工智能 (AI),特别是深度学习 (DL),为分析医疗图像提供了一种强大的方法.
本综述侧重于DL技术用于SARS-CoV-2检测在呼吸道成像到2024年5月.

研究的目的:

为使用医学成像进行SARS-CoV-2诊断提供基于DL的方法的全面审查.
分析各种DL模型对基于成像的SARS-CoV-2检测的适用性.
识别当前的挑战,研究缺口,以及该领域的未来方向.

主要方法:

对用于SARS-CoV-2成像诊断的DL技术进行系统审查.
基于DL模型的功能和性能,对DL模型进行分类和批判性评估.
在SARS-CoV-2成像数据上对12个当代DL模型的实验性评估.

主要成果:

移动NetV3深度学习模型表现出卓越的性能,在检测SARS-CoV-2中达到98.11%的准确性.
对比分析可视化了各种DL模型的性能,突出了优缺点.
确定了包括数据隐私,当前模型的局限性和绩效评估指标在内的关键挑战.

结论:

深度学习模型,特别是MobileNetV3,显示出从医学图像中准确有效地诊断SARS-CoV-2的巨大潜力.
建议进行进一步的研究,以应对现有的挑战,并推进DL方法,以改善医疗图像分析.
未来的方向包括探索新的DL架构和增强数据预处理技术,以获得更好的诊断结果.

关键词:

这是一个CT图像CT图像CT图像CT图像.深度学习是一种深度学习.预处理预处理检测SARS-CoV-2的检测方法图像X射线图像X射线图像X射线图像X射线图像

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