胸部CovidNet:一种有效的DL为基础的方法,用于COVID-19,肺,和肺炎检测使用胸部X射线图像图像
Naeem Ullah1, Javed Ali Khan2, Sultan Almakdi3
1University of Engineering and Technology, 66914, Department of Software Engineering, Taxila, Pakistan; naeemullahfeb1997@gmail.com.
Biochemistry and cell biology = Biochimie et biologie cellulaire
|February 2, 2024
概括
一个新的轻量级深度学习模型,ChestCovidNet,通过胸部X射线准确检测COVID-19. 这种强大的网络为快速疾病识别提供了计算效率高的替代方案,实现了高分类准确性.
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:
- 目前的肺部疾病查是昂贵和耗时的.
- 胸部放射图像 (CRI) 对于快速检测COVID-19至关重要.
- 目前用于COVID-19检测的深度学习 (DL) 模型可能在计算上昂贵,并且在数据集中缺乏稳定性.
研究的目的:
- 提出一个可靠,强大和轻量级的网络 (ChestCovidNet) 用于使用CRI检测COVID-19.
- 评估CestCovidNet在不同CRI数据集上的表现.
- 将ChestCovidNet与混合DL和支持矢量机 (SVM) 方法进行比较.
主要方法:
- 开发了ChestCovidNet,一个轻量级的网络,有11个学习层 (8个卷积,3个完全连接).
- 使用卷积层和组卷积层,泄漏ReLU激活,以及使用3x3和1x1内核的ShuffleNet单元.
- 雇佣了跨道和批量规范化,接受了9013个CRI的培训,并在3863个CRI上进行了测试.
主要成果:
- ChestCovidNet的分类准确率达到了98.12%.
- 该模型表现出高性能,具有回忆,F1得分和95.75%的精度.
- 在分类任务中,ChestCovidNet的性能优于混合DL-SVM方法.
结论:
- 拟议的ChestCovidNet是一个计算效率高和强大的模型,用于从CRIs检测COVID-19.
- 这种轻量级的网络提供了一种可靠的解决方案,用于快速准确地识别COVID-19.
- ChestCovidNet在人工智能驱动的传染病医疗诊断方面取得了有前途的进展.
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