计算机科学 ›› 2022, Vol. 49 ›› Issue (6A): 22-26.doi: 10.11896/jsjkx.210500197
常炳国, 石华龙, 常雨馨
CHANG Bing-guo, SHI Hua-long, CHANG Yu-xin
摘要: 皮肤黑色素瘤是一种早期发现可治愈的疾病。目前诊断黑色素瘤的主要方法是基于皮肤镜的人工目视观察,较易受医师医技水平和经验的影响,诊断准确率为75%~80%,且诊断效率低。对此,文中提出一种融合元数据和图像数据的多模态神经网络算法。元数据是通过感知机学习模型提取的患者基本信息、病灶采集部位、图像分辨率和数量的特征向量;图像数据是通过CNN模型提取的特征向量,把两个特征向量进行融合映射以获得疾病分类结果,用于黑色素肿瘤的早期辅助诊断应用。收集整理了ISIC 2019和ISIC 2020的混合数据集,共58 457条样本数据,训练样本和测试样本按照4∶1比例进行划分,分别采用所提多模态算法和卷积神经网络方法进行对比实验研究,结果表明,使用所提算法构造的黑色素肿瘤辅助诊断分类模型能够将AUC值提升1%左右,证明其具有一定的使用价值。
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