CR-Conformer:用于临床皮肤病变分类的融合网络
Dezhi Zhang1,2,3, Aolun Li4, Weidong Wu5,6,7
1Department of Dermatology and Venereology, People's Hospital of Xinjiang Uygur Autonomous Region, Urumqi, 830000, China.
Medical & biological engineering & computing
|August 31, 2023
概括
一个新的CR-Conformer模型使用临床图像进行皮肤病变诊断,以更少的参数提高1.08%的准确性. 这种深度学习方法在服务不足的地区提供了移动皮肤病诊断的潜力.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 皮肤病学 皮肤病学
背景情况:
- 深层卷积神经网络 (DCNNs) 对皮肤病变诊断有希望,但通常需要昂贵的皮肤镜设备.
- 临床图像更容易获得,特别是在农村或偏远地区,这凸显了使用这些数据的诊断工具的需要.
研究的目的:
- 通过使用随时可用的临床图像,开发一种有效的皮肤病变计算机辅助诊断系统.
- 提高皮肤病变从临床照片分类的准确性和效率.
主要方法:
- 拟议的CR-Conformer是一个双分支的融合网络,集成DCNN用于本地特征和变压器用于全球特征.
- 通过卷积旋转操作增强了DCNN分支,以提取方向特征.
- 在私人XJUSL数据集上验证了该方法,该数据集包含十种类型的临床皮肤病变.
主要成果:
- 该CR-Conformer模型在临床皮肤病变图像分类准确度上取得了1.08%的改进.
- 减少了1117万个模型参数的数量.
- 在临床皮肤病变图像的多种数据集上表现出强的性能.
结论:
- 该CR-Conformer模型有效地利用临床图像进行皮肤病变诊断,优于以前的方法.
- 增强的特征提取和融合方法提高了分类准确性.
- 这种方法有可能为移动设备开发自动皮肤病变诊断工具,提高皮肤病理护理的可用性.
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