卡普奇红优化Swin变压器基于卷积块注意力模块用于肺炎的早期诊断和分类
Shaik Sikindar1, Ch V Raghavendran2, G Madhavi3
1Department of Computer Science & Engineering, Jawaharlal Nehru Technological University Kakinada, Andhra Pradesh, 533003, India.
Current medical imaging
|December 2, 2025
概括
一个新的深度学习模型准确地使用胸部成像对肺炎,COVID-19和正常病例进行分类. 这种人工智能驱动的方法显示出高准确性和速度,为早期疾病检测提供了一个有前途的工具.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 肺炎诊断需要及时和准确的方法来减少患者的发病率和死亡率.
- 从胸部成像中区分肺炎,COVID-19和正常病例至关重要.
研究的目的:
- 开发一种高效的深度学习模型,使用胸部X射线和CT图像对肺炎,COVID-19和正常病例进行分类.
- 为了提高诊断准确度和呼吸道疾病检测的速度.
主要方法:
- 一个混合深度学习模型,将卡普奇红优化 (CRKO) 与基于Swin变压器的卷积区注意模块 (ST-CBAM) 集成.
- 使用Butterworth过器进行预处理,ResNet和视觉变压器进行特征提取,以及适应式门式循环单元 (AGRU) 进行特征融合.
- 在公开的Kaggle胸部X射线数据集上接受培训和验证.
主要成果:
- 实现了高分类准确率:正常的99%,COVID-19的99.9%,肺炎的98.2%.
- 获得曲线下的面积 (AUC) 为98.93%.
- 在准确性和执行时间方面,超越了ACNN,3D-CNN,LWHNN和CA-DCNN等现有模型.
结论:
- CRKO-ST-CBAM模型在自动肺炎诊断方面表现出卓越的性能.
- 该框架显示了在医学成像系统中临床应用的巨大潜力.
- 建议对不同的现实数据集进行进一步验证,以确保可通用性.
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