改进了基于swin变压器的胸部疾病分类,使用胸部X射线进行最佳特征选择
Nadim Rana1, Yahaya Coulibaly2, Ayman Noor3
1Department of Computer Science, College of Engineering and Computer Science, Jazan University, Jazan, Saudi Arabia.
PloS one
|June 25, 2025
概括
这项研究引入了一个深度学习模型,用于从胸部X射线中对胸部疾病进行分类. 这种新的方法在检测肺炎和肺癌等疾病方面取得了很高的准确性.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 计算机辅助诊断 计算机辅助诊断
背景情况:
- 胸部疾病对健康构成重大风险,需要准确及时诊断.
- 目前用于胸部疾病的诊断方法可能耗时,可能缺乏精度.
研究的目的:
- 提出一个深度学习模型,使用胸部X射线准确分类胸部疾病.
- 增强特征提取和选择,以提高诊断性能.
主要方法:
- 胸部X射线图像的预处理,包括大小调整,正常化和数据增强.
- 使用带有注意模块的增强自动编码器 (EnAE) 来提取特征.
- 通过混乱鱼优化 (ChWO) 算法进行特征选择.
- 使用改进的旋转变压器 (IMSTrans) 模型进行分类.
主要成果:
- 拟议的EnAE + ChWO + IMSTrans模型实现了高性能指标.
- 关键绩效指标包括准确性 (0.964),精度 (0.977),回忆 (0.9845),和F-Score (0.964) 等.
- 该模型证明了可靠和高效的胸部疾病分类.
结论:
- 开发的深度学习模型为胸部疾病分类提供了可靠和高效的解决方案.
- 整合EnAE,ChWO和IMSTrans显著提高了诊断准确度.
- 这种方法有望在胸部成像中推进计算机辅助诊断.
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