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洞察耳部疾病的自动图像诊断,基于优化深度学习方法

Heba M Afify^1,2, Kamel K Mohammed^3,4, Aboul Ella Hassanien^5,4,6

¹Systems and Biomedical Engineering Department, Higher Institute of Engineering in Shorouk Academy, Al Shorouk City, Cairo, Egypt. hebaaffify@yahoo.com.

Annals of biomedical engineering

|December 15, 2023

概括

本研究介绍了一种使用卷积神经网络 (CNN) 的自动化工具,用于从耳镜图像中诊断耳部疾病. 人工智能模型实现了高精度,为耳部疾病分类提供了可靠的方法.

科学领域:

医疗成像医学成像
人工智能在医学中的应用
耳鼻喉科耳鼻喉科耳鼻喉科

背景情况:

耳部疾病的临床诊断可能受到限制,需要改进诊断方法.
耳镜图像处理为提高耳部疾病诊断提供了一个有希望的途径.
与传统方法相比,卷积神经网络 (CNN) 在医学诊断中表现出更高的准确性.

研究的目的:

开发和评估使用CNN和贝叶斯超参数优化诊断耳部疾病的自动化系统.
将耳视镜图像分为四个类别:正常,耳硬化,耳塞和慢性中耳炎 (COM).
将拟议方法的性能与耳部疾病分类的既定指标进行比较.

主要方法:

利用CNN架构与贝叶斯超参数优化集成,用于自动诊断.
在一个包含616张口腔镜图像的数据库上训练模型,并在264张测试图像上验证它.
使用准确度,灵敏度,特异性和正预测值 (PPV) 评估分类性能.

主要成果:

获得了高分类准确率的98.10%.
表现出极好的性能,灵敏度为98.11%,特异性为99.36%,PPV为98.10%.
成功确定了最佳的CNN超参数,以准确和时间有效地诊断耳部疾病.

结论:

建议的方法,利用贝叶斯优化和CNN,为耳部疾病诊断提供了一个高度准确和可靠的方法.
这种自动化工具可以显著改善各种耳部疾病的分类和预测.
这些发现支持开发人工智能驱动的工具,用于增强耳鼻喉诊断.

关键词:

贝叶斯优化是贝叶斯的优化.卷积神经网络 (CNN) 是一种神经网络.耳部成像数据库耳部成像数据库超参数超参数是指超参数.

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