连锁修改的LeNet方法用于分类肺炎图像
Dhayanithi Jaganathan1, Sathiyabhama Balsubramaniam1, Vidhushavarshini Sureshkumar2
1Department of Computer Science and Engineering, Sona College of Technology, Salem 636005, India.
Journal of personalized medicine
|March 28, 2024
概括
一个新的深度学习模型,连锁修改的LeNet分类器,准确地从医疗图像中检测出肺炎,准确率为96%. 这一进步为提高患者护理和及时治疗提供了高效的肺炎诊断.
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:
- 肺炎是一个重要的全球健康问题,需要先进的诊断方法.
- 目前用于肺炎的诊断工具可以通过更有效,更准确的技术来改进.
- 深度学习为增强医学图像分析和疾病检测提供了潜力.
研究的目的:
- 开发和评估一个深度学习模型,用于准确的肺炎图像分类.
- 提高卷积神经网络 (CNN) 架构的辨别能力和性能,用于肺炎诊断.
- 评估修改后的LeNet架构的有效性,包括ReLU和批量规范化.
主要方法:
- 使用深度学习实现连接修改的LeNet分类器.
- 整合了修改后的 Rectified Linear Unit (ReLU) 激活功能,以增强功能学习.
- 集成批量规范化以稳定训练和提高CNN的性能.
主要成果:
- 连锁修改的LeNet分类器在肺炎图像识别中实现了96%的准确率.
- 该模型在与其他深度学习模型进行比较时显示出高的识别率.
- 修改,包括ReLU和批量规范化,有助于防止过拟合和减少计算时间.
结论:
- 连锁修改LeNet分类器显示出作为医疗专业人员诊断肺炎的工具的巨大潜力.
- 通过模型准确和高效的图像分类可以导致更好的治疗决策和患者的结果.
- 这种深度学习方法有助于提高肺炎的诊断能力.
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