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在KUB X射线图像中识别结石,使用VGG16授权使用可解释的人工智能

Fahad Ahmed¹, Sagheer Abbas², Atifa Athar³

¹School of Computer Science, National College of Business Administration and Economics, Lahore, 54000, Pakistan.

Scientific reports

|March 15, 2024

概括

这项研究开发了一个使用VGG16和可解释AI (XAI) 的AI系统,以97.41%的精度在X射线中检测结石. XAI提高了透明度,帮助医生准确识别结石.

科学领域:

医疗成像医学成像
人工智能的人工智能
泌尿器科泌尿器科泌尿器科泌尿器科

背景情况:

结石会引起严重的疼痛,并可能导致由于尿路堵塞而导致功能衰竭.
对结石的脏-尿道-膀 (KUB) X射线图像的解释对医学专家来说是具有挑战性和耗时的.
准确高效的检测系统对于及时诊断和治疗结石至关重要.

研究的目的:

开发一种人工智能驱动的系统,准确地将KUBX射线图像分类为结石或正常病例.
提高医疗图像分析中人工智能模型的透明度和公平性.
研究可解释AI (XAI) 在协助临床医生检测结石方面的作用.

主要方法:

应用一个转移学习 (TL) 模型,使用预先训练的VGG16架构.
集成层级相关性传播 (LRP),一种可解释的人工智能 (XAI) 技术,与VGG16模型.
KUB X射线图像的分类,以区分结石和正常病例.

主要成果:

开发的系统在识别结石或正常KUBX射线时,达到97.41%的测试准确率.
纳入LRP提高了VGG16模型预测的公平性和透明度.
可解释的人工智能 (XAI) 促进了人类对模型决策过程的理解.

结论:

人工智能系统在从KUBX射线检测结石时表现出高准确度.
像LRP这样的可解释AI (XAI) 技术对于建立对AI驱动医学诊断的信任和理解至关重要.
XAI可以显著帮助医疗专业人员准确识别结石,从而改善治疗策略.

关键词:

人工智能 (AI) 是一种人工智能.卷积神经网络 (CNN) 是一种神经网络.深度学习 (DL) 是指深度学习.结石,可解释的人工智能 (XAI)- 尿道 - 膀 (KUB) 的情况层层的相关性传播 (LRP)机器学习 (ML) 是指机器学习.转移学习 (TL) 是指转移学习.在VGG16中,VGG16是VGG16中的一个.

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