布-E-Yolov5s网络用于识别小肠小性病变亚型
Xudong Guo1, Liying Pang1, Lei Xu1
1School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
The Review of scientific instruments
|March 18, 2025
概括
准确诊断小肠是具有挑战性的. 一个新的AI模型Cascade-E-Yolov5s显著改善了子亚型的立即检测和分类,有助于临床诊断.
科学领域:
- 胃肠病学和人工智能的人工智能
- 医学成像和诊断 医学成像和诊断
- 计算病理学计算病理学
背景情况:
- 由于病变的复杂性和分布,精确诊断小肠很困难,往往导致诊断延迟.
- 目前的诊断方法依赖于病理和随访,限制了立即诊断的准确性.
- 需要先进的工具来提高早期检测的效率和精度.
研究的目的:
- 引入和评估布-E-Yolov5s网络,以加强小肠亚型的立即诊断.
- 提高在内镜手术过程中识别不同类型小肠的准确性和效率.
- 提供一种计算工具,帮助临床医生更快,更准确地进行诊断.
主要方法:
- 开发 Cascade-E-Yolov5s 网络,集成图像分类的 EfficientNet 和用于病变检测的 SimAM-Yolov5.
- 使用EfficientNet作为SimAM-Yolov5s的骨干,结合SIoU损失和无参数注意模块.
- 在684名患者的4909张图像数据集上进行训练和测试模型,涵盖四种不同的类型.
主要成果:
- 布-E-Yolov5s网络实现了平均检测精度为86.46%.
- 该模型在0.5的IOU值 (mAP@0.5) 获得了82.20%的平均平均精度.
- 与传统检测网络相比,Cascade-E-Yolov5s在识别子亚型方面表现优越.
结论:
- Cascade-E-Yolov5s网络有效地提高了小肠亚型的检测效率和准确性.
- 这种人工智能模型在帮助临床医生在内镜检查过程中更准确和及时诊断方面表现出有希望.
- 这项研究强调了先进的深度学习模型在克服胃肠病学诊断挑战方面的潜力.
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