石网:基于深度可分离卷积的高效轻量级模型,用于从CT图像中检测结石
Sohaib Asif1, Ming Zhao2, Xuehan Chen3
1School of Computer Science and Engineering, Central South University, Changsha, China.
Interdisciplinary sciences, computational life sciences
|July 15, 2023
概括
一个新的人工智能 (AI) 模型StoneNet使用MobileNet有效检测结石,为早期诊断提供低成本,高精度的解决方案,并可能预防功能衰竭.
科学领域:
- 生物医学工程 生物医学工程
- 医学成像分析分析 医学成像分析
- 医疗保健中的人工智能
背景情况:
- 结石疾病是全球普遍存在的严重健康问题,通常会引起严重的疼痛并导致住院治疗.
- 早期诊断结石对于预防功能衰竭等并发症至关重要,但检测小结石仍然具有挑战性.
- 虽然人工智能 (AI) 在医学诊断方面表现有前途,但现有的结石检测深度学习模型遭受了高计算成本和长时间的培训时间.
研究的目的:
- 开发一种轻量级,高性能的人工智能模型,以高效准确地检测结石.
- 解决现有的深度学习模型的局限性,例如高计算成本和大参数数量.
- 在医疗决策支持系统中,为结石诊断提供具有成本效益的解决方案.
主要方法:
- 提出了"StoneNet",这是一个基于MobileNet架构的新型AI模型,利用深度可分离的卷积.
- 集成的关键组件包括全球平均聚合 (GAP),批量规范化,脱落层和密集层.
- 基准测试StoneNet与四个预训练模型和一个最先进的重型模型相比,使用了1799张CT图像的数据集.
主要成果:
- 石网实现了最高准确率97.98%,与平面化层相比,参数显著降低,证明了改进的模型稳定性.
- 该模型在计算上表现出更快的性能,训练和测试时间分别为996.88s和14.62s.
- 实验证实了StoneNet在精度和复杂性方面的卓越表现,与其他评估模型相比,它在一个大型脏数据集上具有较低的复杂性.
结论:
- 石网为结石检测提供了一个计算效率高,准确度高的解决方案.
- 该模型的轻量级设计和最佳性能使其适合实时应用,并协助放射科医生更快地进行诊断.
- 开发的AI模型具有很大的潜力,可以在临床环境中部署,以改善结石疾病的管理.
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