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基于深度学习的超声波图像对良性皮肤瘤的评估

Hyunwoo Lee¹, Yerin Lee², Seung-Won Jung³

¹Department of Biomedical Engineering, Yonsei University, Wonju 26493, Republic of Korea.

Sensors (Basel, Switzerland)

|September 9, 2023

概括

一种新的深度学习模型使用超声波图像准确诊断三种类型的良性皮肤瘤. 这种联合卷积神经网络 (CNN) 显示出作为医生的诊断辅助工具的潜力,提高了准确性和效率.

科学领域:

皮肤病学皮肤病学
人工智能的人工智能
医疗成像医学成像

背景情况:

准确诊断良性皮肤瘤对于适当的患者管理至关重要.
当前的诊断方法可能是主观的,耗时的.
深度学习为客观和高效的图像分析提供了潜力.

研究的目的:

设计和验证一个联合卷积神经网络 (CNN),用于诊断三种类型的良性皮肤瘤.
评估开发的CNN模型的诊断性能.
为了比较CNN的诊断标准与经验丰富的医生.

主要方法:

开发了一个新的CNN架构,整合了剩余和注意力结构.
收集和分析了698张良性皮肤瘤的超声波图像.
该模型使用五倍交叉验证和数据增强与快速自动增强进行了训练和验证.

主要成果:

综合CNN的平均准确率为95.87%,平均灵敏度为90.10%,平均特异性为96.23%,用于诊断三个良性皮肤瘤.
类激活映射表明CNN和医生之间存在类似的诊断标准.
统计分析证实了模型的可靠性和一致性.

结论:

开发的综合CNN在通过超声波图像诊断良性皮肤瘤方面表现出高准确度和可靠性.
该模型显示出作为皮肤病学家有效的计算机辅助诊断工具的前景.
这种人工智能驱动的方法可以提高临床实践中的诊断效率和准确性.

关键词:

良性皮肤瘤是一种良性皮肤瘤.班级激活地图的地图.卷积神经网络是一种卷积神经网络.超声波图像超声波图像超声波图像

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