肺部疾病的精确识别使用适应性多尺度特征融合在胸部X射线图
Mengran Zhou1, Lipeng Gao2, Kai Bian1
1School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan, 232001, Anhui, China.
Scientific reports
|August 10, 2025
概括
这项研究引入了自适应多尺度融合网络 (AMFNet),以改善胸部X射线中肺部疾病的检测. AMFNet准确地识别了肺炎,结核病和COVID-19,以高精度和更少的参数优于现有模型.
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:
- 肺部疾病对健康构成重大风险,并且由于解剖学复杂性,难以通过胸部X射线进行诊断.
- 准确有效地检测肺炎,肺结核和COVID-19等常见肺部疾病对于及时治疗至关重要.
研究的目的:
- 开发和评估一个可适应的多尺度融合网络 (AMFNet),以在胸部X射线图像中加强肺部疾病的分类.
- 提高AI模型在识别常见呼吸道感染方面的准确性和效率.
主要方法:
- 提出了一个可适应的多级融合网络 (AMFNet),集成一个轻量级的多级融合网络 (MFNet) 和ResNet50.
- MFNet使用自校准卷积 (SCConv) 和注意特征融合 (AFF) 来进行多尺度特征提取,以及自定义的MFReLU激活功能.
- 融合模块适应性地结合了MFNet和ResNet50的功能,以实现强大的分类.
主要成果:
- 在公共胸部X射线数据集上,AMFNet获得了97.48%的高精度和0.9781的F1得分.
- 拟议的模型的性能优于已有的模型,包括ResNet50,DenseNet121,ConvNeXt-Tiny和Vision Transformer.
- 与其他模型相比,AMFNet在减少参数数量的情况下表现出卓越的性能.
结论:
- AMFNet提供了一个有前途的方法,可以通过胸部X射线准确有效地分类肺部疾病.
- 该模型能够捕捉多个尺度的语义特征并适应地融合信息,这有助于其卓越的性能.
- 这种人工智能驱动的方法有可能帮助临床医生诊断严重的肺部疾病.
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