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TurkerNeXtV2:一种创新的CNN模型用于膝关节骨关节炎压力图像分类

Omer Esmez¹, Gulnihal Deniz², Furkan Bilek³

¹Department of Orthopedics, Elazig Fethi Sekin City Hospital, Elazig 23280, Turkey.

Diagnostics (Basel, Switzerland)

|October 16, 2025

概括

TurkerNeXtV2是一种全新的轻型卷积神经网络 (CNN),在医学成像中以高精度和高效率实现了变压器级性能. 这种紧型号适用于实时临床应用.

科学领域:

计算机视觉计算机视觉
医疗成像医学成像
人工智能的人工智能

背景情况:

医疗成像应用的轻量级卷积神经网络 (CNN) 是有限的.
现有的模型往往难以平衡有效性和计算效率.

研究的目的:

为了介绍TurkerNeXtV2,一个紧的CNN设计用于医学成像.
使用CNN的简单性和低计算成本,实现变压器级的有效性.
通过新的架构块来提高模型稳定性和效率.

主要方法:

开发了TurkerNeXtV2的两个新区块:基于聚合的注意力与反向瓶 (TNV2) 和混合降低采样模块.
在稳定图像网-1k基准上预训练模型.
微调并根据脚部压力骨关节炎 (OA) 数据集和血细胞图像数据集进行评估.
使用精度,精度,回忆,F1得分和推断时间 (图像/秒) 测量性能.

主要成果:

在稳定图像网-1k的预训练中实现了87.77%的验证准确性.
在OA数据集上达到93.40%的准确性,精度和回忆率超过90%.
在血液细胞数据集上达到98.52%的准确性.
显示每张图像的平均推断时间为0.0078秒 (≈128.8图像/秒),超过了变压器基线.

结论:

在医疗成像任务中,TurkerNeXtV2提供了高精度和低计算成本.
基于聚合的注意力 (TNV2) 和混合下方采样有助于轻量化但有效的设计.
该模型适合实时和临床部署.

关键词:

土耳其人NeXtV2 生物医学图像分类的分类.深度学习是一种深度学习.骨关节炎检测检测器基于聚合关注的注意力.

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