基于CNN的连锁性肺炎检测使用模糊增强数据集
Abror Shavkatovich Buriboev1,2, Dilnoz Muhamediyeva3, Holida Primova4
1School of Computing, Department of AI-Software, Gachon University, Seongnam-si 13306, Republic of Korea.
Sensors (Basel, Switzerland)
|October 26, 2024
概括
这项研究引入了一种新的连锁CNN模型,与模糊逻辑图像增强相结合,以改进肺炎检测. 改进后的模型实现了高准确度,精度和回忆,提供了更快,更有效的诊断工具.
科学领域:
- 医疗成像医学成像
- 人工智能在医学中的应用
- 呼吸系统医学 呼吸系统医学
背景情况:
- 由于类似的病毒和细菌症状,肺炎的诊断具有挑战性.
- 目前可靠的方法,如PCR,需要几个小时和特殊的条件.
- 需要快速,准确和易于使用的诊断工具至关重要.
研究的目的:
- 开发一个先进的深度学习模型用于肺炎检测.
- 为了提高医疗图像质量,使用模糊逻辑来提高诊断准确度.
- 将基于模糊逻辑的图像增强与连锁CNN (CCNN) 模型集成.
主要方法:
- 开发了一种基于模糊逻辑的图像改进算法.
- 该算法改善了CCNN模型的图像质量和特征提取.
- 四个数据集 (原始和增强图像) 用于训练CCNN.
主要成果:
- 通过增强数据集,CCNN模型显著提高了性能.
- 模糊增强数据集产生了最好的结果.
- 实现了高分类指标:98.9%的准确性,99.3%的精度,99.8%的F1分数,99.6%的回忆.
结论:
- 基于模糊逻辑的图像增强显著提高了CCNN模型用于肺炎检测的性能.
- 拟议的方法优于传统的图像增强技术.
- 这种综合方法为精确高效的医学图像分析提供了有前途的解决方案.
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