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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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基于智能手机的口腔损伤图像细分使用深度学习.

Tapabrat Thakuria1,2, Lipi B Mahanta3,4, Sanjib Kumar Khataniar5

  • 1Mathematical and Computational Science Laboratory, Physical Science Division, Institute of Advanced Study in Science and Technology (IASST), Paschim Boragaon, Garchuk, Guwahati, 781035, Assam, India.

Journal of imaging informatics in medicine
|March 3, 2025
PubMed
概括
此摘要是机器生成的。

一个新的深度学习模型,OralSegNet,准确地从智能手机图像中对口腔病变进行细分,以早期检测疾病. 这种具有成本效益的解决方案提高了诊断的准确性,特别是在服务不足的地区.

关键词:
卷积神经网络是一个卷积神经网络.深度学习是一种深度学习.医疗图像分析 医学图像分析口腔疾病是一种口腔疾病.口腔病变的细分口腔病变的细分基于智能手机的成像技术

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科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 计算生物学 计算生物学

背景情况:

  • 早期发现口腔疾病对于改善患者的治疗结果至关重要.
  • 精确的口腔病变细分有助于临床医生,并增强深度学习 (DL) 诊断模型.
  • 基于智能手机的成像为可访问的口腔疾病查提供了潜在的途径.

研究的目的:

  • 开发一种深度学习 (DL) 解决方案,用于从智能手机拍摄的图像中对口腔病变进行细分.
  • 设计和评估一种基于UNet的新型模型,OralSegNet,以提高细分精度.
  • 为早期口腔疾病诊断提供一个具有成本效益和非侵入性的工具.

主要方法:

  • 一个基于UNet的新型模型,OralSegNet,是使用EfficientNetV2L作为编码器设计的,它结合了Atrous空间金字塔聚合 (ASPP) 和剩余块.
  • 采用了538张口腔病变图像的数据集,进行了预处理,将大小改为256x256像素,并增强了强度.
  • 模型的性能被评估使用子系数和交叉与欧盟 (IoU) 在验证和测试集上的分数.

主要成果:

  • 口腔SegNet实现了高性能与0.9530 (验证) 和0.8518 (测试) 的子系数,和0.9104 (验证) 和0.7550 (测试) 的IOU分数.
  • 该模型的表现优于传统和最先进的细分模型.
  • 尽管参数数量有限,但OralSegNet的计算效率是最低的FLOPS (34.30 GFLOPS).

结论:

  • OralSegNet提供了基于深度学习的准确有效的解决方案,用于从智能手机图像中对口腔病变进行细分.
  • 该模型的性能和成本效益使其成为临床医生的宝贵工具,促进早期诊断.
  • 这项技术有可能提高口腔疾病诊断的可访问性,特别是在农村或资源有限的环境中.