一个先进的肺癌预测和风险查模型,使用转移学习
Isha Bhatia1, Aarti1, Syed Immamul Ansarullah2
1Department of Computer Science and Engineering, Lovely Professional University, Phagwara 144001, India.
Diagnostics (Basel, Switzerland)
|July 13, 2024
概括
这项研究引入了一种先进的肺癌预测模型,使用转移学习用于早期肺癌检测. 该模型在识别和评估风险方面取得了很高的准确性,改进了当前的预测方法.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 在瘤学瘤学.
背景情况:
- 肺癌 (肺癌) 具有显著的死亡风险,强调了早期诊断的必要性.
- 目前的肺癌预测模型在医疗图像中的准确性,噪音和低对比度等方面存在问题.
- 有效的早期检测和风险评估对于改善患者结果至关重要.
研究的目的:
- 提出一个先进的肺癌预测和风险查模型,利用转移学习.
- 解决现有模型的局限性,包括计算机断层扫描 (CT) 图像的低精度,噪声和低对比度.
- 为了提高早期肺癌诊断和风险分层.
主要方法:
- 肺部CT图像的预处理:消除噪音,对比度拉伸,凸体肺部区域提取和边缘增强.
- 使用修改后的贝茨分布和coati优化 (B-RGS) 算法进行图像细分,用于特征提取.
- 使用PResNet分类器将肺癌分类为正常或异常,然后进行风险查 (低风险/高风险).
主要成果:
- 拟议的模型实现了与最先进的方法可比的高性能指标.
- 获得了98.21%的准确性,98.71%的精度,97.46%的回忆率.
- 在早期肺癌预测和风险评估方面已证明有效.
结论:
- 开发的转移学习模型为早期肺癌检测提供了高效和有效的解决方案.
- 该方法在提高肺癌风险查的准确性和可靠性方面显著有前途.
- 这种方法验证了先进的人工智能技术在增强瘤诊断方面的潜力.
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