在小肠中的性病变的自动分类使用densenet与道注意力和残留扩张块
Xudong Guo1, Lei Xu1, Zhang Liu1
1School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, People's Republic of China.
Physics in medicine and biology
|February 5, 2024
概括
一个新的AI模型DRCA-DenseNet169准确地识别了各种小肠,如克罗恩病 (CD) 和肠结核病 (ITB). 这种工具有助于快速,初步诊断,改善患者对这些常见疾病的护理.
科学领域:
- 胃肠病学 胃肠病学
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 小肠,包括克罗恩病 (CD),肠结核 (ITB),小肠原发性淋巴瘤 (PSIL),密码多焦点性狭窄性肠炎 (CMUSE) 和非特异性 (NSU),是常见的,但由于形态相似性,通过肠镜诊断具有挑战性.
- 准确区分这些条件对于有效的治疗和患者的结果至关重要.
研究的目的:
- 开发和评估一个智能系统,DRCA-DenseNet169,用于准确识别五种类型的小肠.
- 为了应对基于肠镜图像的诊断小肠的挑战.
主要方法:
- 使用DenseNet169架构开发了一个深度学习模型DRCA-DenseNet169,其中包含了剩余扩展块和道注意力块.
- 设计了一种新的动态加权损失函数,以提高有限样本不平衡数据集的性能.
- 该模型在上海长海医院的10,883张肠镜图像数据集上进行了训练和验证,其中包括5,375张图像和5,508张正常图像.
主要成果:
- DRCA-DenseNet169实现了高整体准确度,达到85.27% ± 0.32%.
- 该模型表现出强的性能,加权精度为83.99% ± 2.47%,加权回忆率为84.36% ± 0.88%,加权F1得分为84.07% ± 2.14%.
- 该系统在区分不同类型的小肠时表现出高的识别准确性和稳定性.
结论:
- DRCA-DenseNet169提供了一个有前途的工具,用于立即和初步诊断小肠.
- 开发的AI模型可以帮助临床医生区分复杂的形态,可能导致更早,更准确的治疗策略.
- 这种人工智能驱动的方法有可能提高胃肠病学中的诊断效率和准确性.
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