基于深度学习的预测模型用于使用内镜图像诊断胃肠道疾病
Anju Sharma1, Rajnish Kumar2, Prabha Garg1
1Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research, S.A.S. Nagar, Punjab 160062, India.
使用卷积神经网络 (CNN) 的人工智能 (AI) 显示出对诊断胃肠道疾病的希望. 一个ResNet50模型在检测医疗图像中的息肉,性结肠炎和食道炎方面达到99%以上的准确性.
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) 感染在全球普遍存在.
- 目前的诊断方法,如结肠镜和无线囊内镜 (WCE),耗时且容易出现人为错误.
- 基于人工智能的自动诊断工具对于改善早期检测和患者护理至关重要.
研究的目的:
- 开发和评估基于人工智能的系统,用于早期诊断胃肠道疾病.
- 使用卷积神经网络 (CNN) 提高诊断准确度.
- 评估不同CNN模型的性能,包括转移学习方法.
主要方法:
- 在KVASIR数据集上训练了各种CNN模型 (基线,VGG16,InceptionV3,ResNet50).
- 使用n倍交叉验证和数据增强策略.
- 在1200张图像的测试集上评估模型性能,包括健康和疾病状态 (多囊,性结肠炎,食道炎).
主要成果:
- 在ResNet50预训练的CNN模型实现了最高的准确性 (约. 在培训上占99.80%,在验证上占99.50%,在测试集上占99.16%.
- 该ResNet50模型显示了高精度 (100%) 和回忆 (约. 99%) 在诊断胃肠道疾病.
- 拟议的ResNet50模型在诊断准确性方面表现优于现有系统.
结论:
- 基于人工智能的预测模型,特别是像ResNet50这样的CNN,显著提高了胃肠道疾病的诊断准确性.
- 开发的模型有助于早期检测胃肠,性结肠炎和食道炎.
- 该预测模型是公开可用的,用于进一步的研究和应用.
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