一个两阶段的转移学习框架对于胃肠道疾病的分类
Ahmed Ali1, Arshad Iqbal1,2, Sohail Khan2
1School of Computing Sciences, Pak-Austria Fachhochschule: Institute of Applied Sciences and Technology, Mang, Haripur, Khyber Pakhtunkhwa, Pakistan.
PeerJ. Computer science
|February 3, 2025
概括
本研究介绍了一种使用转移学习的AI框架,以从内镜图像改善胃肠道 (GI) 疾病的诊断. InceptionResNetV2模型在识别各种GI疾病方面表现出高准确性,有助于早期检测.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 胃肠病学 胃肠病学
背景情况:
- 胃肠道 (GI) 疾病很普遍,并且可能是令人虚弱的.
- 人工智能 (AI),特别是计算机视觉,在医学图像分析方面表现有前途.
- 深度卷积神经网络 (CNN) 擅长识别图像中的复杂模式.
研究的目的:
- 开发和评估一个人工智能驱动的,双相转移学习框架,用于从内镜图像中增强肠道疾病的识别.
- 帮助胃肠科医生实现更有效,更准确的诊断.
- 为微调预先训练的模型和训练定制的CNN进行比较性能分析.
主要方法:
- 使用了两阶段的转移学习框架.
- 在注释的GI内镜图像数据集上微调了三个预训练模型 (Xception,InceptionResNetV2,VGG16).
- 构建并训练了两个定制的CNN,以便进行比较.
- 在四个不同的分类任务中评估性能.
主要成果:
- InceptionResNetV2架构在各个任务中实现了高精度:85.7% (8类GI分类),97.6% (3类疾病分类),99.5% (多体识别) 和74.2% (食道炎严重程度).
- 转移学习框架表现出一致和普遍的表现.
- 人工智能有效地从未见过的内镜图像中识别出胃肠道疾病.
结论:
- 拟议的两阶段转移学习框架是有效的临床应用在GI疾病的识别.
- 人工智能辅助诊断可以提高早期检测和治疗胃肠道疾病.
- 这项研究强调了人工智能在改善胃肠道诊断方面的潜力.
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