OralSegNet:一种使用转移学习的口腔疾病早期检测方法
Pranta Barua1, Md Rakibul Islam2,3, Mahmud Uz Zaman4
1Department of Computer Science and Engineering, University of Chittagong, Chittagong, Bangladesh.
Oral diseases
|November 9, 2025
概括
使用YOLOv11变体的深度学习系统自动从口腔内图像中检测口腔疾病. YOLOv11m-seg模型实现了高精度,使得一个用户友好的网络应用程序能够为临床医生和非专家提供快速诊断.
科学领域:
- 计算机视觉 计算机视觉
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 口腔疾病对健康构成重大挑战,需要准确及时检测.
- 自动诊断工具可以提高口腔保健的可访问性和效率.
研究的目的:
- 开发和评估基于深度学习的细分系统,用于口腔疾病的自动检测和定位.
- 为了研究三个YOLOv11架构变体 (YOLOv11n-seg,YOLOv11s-seg,YOLOv11m-seg) 在口内图像上的性能.
主要方法:
- 通过多个版本 (v1-v3) 策划和增强了口内图像的数据集,以解决阶级不平衡并增强概括性.
- 采用了三个阶段的培训:特征提取,部分微调和全面微调.
- 在部分微调阶段,YOLOv11m-seg模型表现出卓越的性能.
主要成果:
- 在部分微调过程中,YOLOv11m-seg模型实现了0.521的盒子mAP@50和0.500的面罩mAP@50.
- 培训是在Google Colab的免费层进行的,利用可用的计算资源.
- 性能最好的模型已成功导出到ONNX格式,并启用NMS.
结论:
- 开发的深度学习系统有效地从口腔内图像中检测和定位口腔疾病.
- 该系统作为客户端Web应用程序使用React.js和ONNX运行时Web进行部署,以实现广泛的可访问性.
- 该工具使临床医生和非专家能够通过单击快速检测口腔疾病.
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