Computer assisted diagnosis using artificial neural network

口腔癌患者颈淋巴结转移的存在具有重要的预后和治疗意义。淋巴结是转移性的还是反应性的标准还没有很好地建立。同时，人工神经网络最近引入诊断图像的分析可能有光明的前景。本研究的目的是探讨人工神经网络在超声图像上诊断淋巴结转移的准确性，共对188个淋巴结进行了研究，所有淋巴结均经组织学证实。在研究的第一部分中，使用了随机选择的138个节点。其中转移性淋巴结60个，反应性淋巴结78个。超声检查设备为PT 2600型超声扫描仪，使用7.5MHz B型线阵探头。评价了7个超声特征，即中央回声门，周围实质区回声，周围实质区均匀性，边缘，边界，最大最小值比和最小直径。为了证明超声图像特征对诊断准确性的影响，使用了各种网络结构和超声图像特征。根据第一部分的研究结果，我们使用Visual C++开发了报表系统。结果表明，中心回声、周围实质回声和Max-Mm比值是诊断转移的最重要指标。神经网络辅助报告系统提高了非熟练医生的诊断能力。结论：神经网络可以辅助诊断颈部淋巴结肿大。

The presence of cervical lymph node metastases in patients with oral cancer is of great prognostic and therapeutic importance. The criteria whether the node is metastatic or reactive has not been well established. Meanwhile, artificial neural networks recently introduced in the analysis of diagnostic images may have bright prospects. The purpose of this study is to determine the accuracy indiagnosing the lymph node metastases on the ultrasonographic images using artificial neural network.Total of 188 nodes was used to the studies, all of which were verified histologically. In first part of the study 138 nodes randomly selected were used. Included among them were 60 nodes metastatic and 78 reactive. An ultrasonographic apparatus was model PT 2600 US scanner using 7.5 MHz B-mode linear scan probe. Seven ultrasonographic features evaluated were central echogenic hilus, echogenity of peripheral parencymal zone, homogeneity of peripheral parencymal zone, margin, border, Max-Min ratio and smallest diameter. To prove the effect of ultrasonographic features on diagnostic accuracy, a variety of network structure and ultrasonographic features were used. According to the result of the first part of the study, we made reporting system using Visual C++. The residual 50 nodes were used for the study in which we examined the ability of the reporting system.The results showed that central echogenic hilus, the echogenity of peripheral parencyma and Max-Mm ratio were most important features on diagnosis of metastasis. The neural network assisted reporting system improved the diagnostic ability of the unskilled doctors. In conclusion, the neural network may assist the diagnosis of cervical lymph node swelling.