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在放射图像中使用各种卷积神经网络架构对瘤和囊的自动细分进行系统审查

Chintam Anusha¹, Kunjam Nageswara Rao¹, T Lakshmana Rao²

¹Department of Computer Science and Systems Engineering, Andhra University College of Engineering (A), Andhra University, Vishakhapatnam, Andhra Pradesh, 530003, India.

Computers in biology and medicine

|October 5, 2025

概括

早期诊断癌是非常重要的. 本综述探讨了深度学习,特别是卷积神经网络 (CNN) 模型,用于医疗成像中的自动质量细分,提高诊断精度.

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:

准确的癌诊断依赖于医学成像 (CT,MRI,US) 和组织病理学.
在放射学图像中手动细分质量是具有挑战性和耗时的.
越来越需要自动化细分方法来提高诊断效率.

研究的目的:

审查使用深度学习模型用于早期质量检测的研究.
分析卷积神经网络 (CNN) 模型在放射图像细分中的应用.
检查自动化细分方法的数据集特征,准确性和效率指标.

主要方法:

对利用深度学习进行质细分的研究进行系统审查.
分析各种卷积神经网络 (CNN) 架构及其性能.
评估数据集大小及其对模型准确性的影响.

主要成果:

深度学习,特别是CNN,显示了自动质量细分的重大前景.
性能因数据集大小和特定模型架构而异.
目前的方法在完全确定瘤和囊诊断方面仍然面临挑战.

结论:

使用深度学习的自动质量细分为早期癌检测提供了一个有前途的途径.
需要对更大,更多样化的数据集进行进一步的研究,以提高诊断能力.
标准化的评估指标对于比较不同的方法至关重要.

关键词:

分类分类分类分类.卷积神经网络是一种卷积神经网络.深度学习是一种深度学习.放射性成像学成像学脏质量脏质量分段化分段化分段化分段化

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