基于稠密连接的多形性腺瘤辅助诊断

Auxiliary Diagnosis of Pleomorphic Adenoma Based on Dense Connection

针对多形性腺瘤诊断完全依赖人工的问题,提出一种计算机辅助诊断方法.先通过采集数据并构建多形性腺瘤数据集,对当前稠密连接网络进行改进并融合通道注意力机制进行疾病组织分类特征提取,得到组织类别和概率,然后使用CART(classification and regression tree)进行推理学习,得到诊断结果.对难判断的类别选择进行人工辅助,进而实现对多形性腺瘤疾病的计算机辅助工作.实验结果表明,该方法在分类识别模块分类提取准确率达97.7%,决策树推理诊断准确率达100%.此外,分类识别模块在血细胞分类领域的准确率达98.6%.该方法具有一定的迁移性和有效性.

Aiming at the problem that the diagnosis of pleomorphic adenoma completely relied on manual labor,we proposed a computer-assisted diagnostic method.Firstly,by collecting data and constructing a pleomorphic adenoma dataset,the current dense connection nertwork was improved and fused with the channel attention mechanism for disease tissue classification feature extraction to obtain tissue categories and probabilities.Secondly,by using classification and regression trees(CART),we obtained diagnostic results and provided manual assistance in the selection of difficult categories,thus achieving computer-assisted work on pleomorphic adenomatous diseases.The experimental results show that the method achieves classification extraction accuracy of 97.7%in the classification recognition module,and decision tree inference diagnostic accyracy of 100%.In addition,the accuracy of classification recognition module achieves 98.6%in the field of blood cell classification,and the method has certain transferability and validity.