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提高自动脑瘤检测准确度使用人工智能对医疗保健环境的方法

Akmalbek Abdusalomov¹, Mekhriddin Rakhimov², Jakhongir Karimberdiyev²

¹Department of Computer Engineering, Gachon University, Sujeong-gu, Seongnam-si 13120, Gyeonggi-do, Republic of Korea.

Bioengineering (Basel, Switzerland)

|June 27, 2024

概括

这项研究使用YOLOv5和非局部神经网络 (NLNNs) 提高了大脑瘤检测,提高了医学成像的准确性. 组合模型显示更高的召回率,有助于早期癌症诊断.

科学领域:

医疗成像医学成像
人工智能的人工智能
在瘤学瘤学.

背景情况:

早期发现脑瘤对于患者的治疗结果至关重要.
深度学习模型在分析医疗图像以诊断癌症方面表现有前途.
现有的方法可能缺乏可靠的瘤识别所需的稳定性和准确性.

研究的目的:

研究YOLOv5与非局部神经网络 (NLNNs) 的集成,以改善脑瘤检测.
为了提高深度学习模型的准确性和稳定性,从MRI扫描中识别脑瘤.
探索用于验证诊断决策的组合模型的可解释性.

主要方法:

一个全面的脑部MRI扫描数据集被策划.
在一个统一的框架中,YOLOv5与NLNNs,K-means+和SPPF+模块集成.
应用转移学习来适应YOLOv5用于脑瘤检测.

主要成果:

与单独使用YOLOv5相比,YOLOv5和NLNNs组合模型显示了增强的检测能力.
综合模型的召回率为86%,仅YOLOv5的召回率为83%.
来自NLNN的注意力图可视化了瘤区域,有助于模型的解释性.

结论:

YOLOv5与NLNNs的整合显著提高了脑瘤检测的准确性和稳定性.
综合模型通过可解释的注意力机制提供了增强的诊断支持.
通过超参数调整和数据增强进行进一步优化,可以进一步提高性能.

关键词:

这就是为什么MRI是MRI.这是YOLOv5的.大脑瘤是个大脑瘤深度学习是一种深度学习.非局部神经网络的非局部神经网络对象检测检测对象检测对象检测细分化细分化的细分化

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