文章摘要

文章信息/Info

Title:: Study of mediastinal lymph node classification based on convolutional neural network

作者:: 程龙1; 秦航1; 余晶1; 蒋红兵2 ; 宋宁宁1△; （1.南京医科大学附属南京医院（南京市第一医院）临床医学工程处，南京 210006；2.南京市急救中心，南京 210003）

Author(s):: CHENG Long1; QIN Hang1; YU Jing1; JIANG Hongbing2; SONG Ningning1; (1.Department of Medical Engineering，Nanjing First Hospital，Nanjing Medical University，Nanjing 210006，China 2.Nanjing Emergency Center, Nanjing 210003)

Keywords:: Lung cancer staging; Lymph node metastasis; Bronchoscopy; Artificial intelligence; ResNet34; Auxiliary diagnosis

摘要:: 为提高纵隔淋巴结良恶性分类的准确率及效率，本研究利用残差网络（residual network ，ResNet）结合迁移学习方法，设计了一种纵膈淋巴结良恶性自动分类算法。实验结果显示，该方法的分类准确率、灵敏度、特异度可达75.1%、78.6%、84.4%，相比传统方法，分别提高了6%、9.9%、15.4%。本研究设计的多纵膈淋巴结自动分类算法相较于传统方法性能有显著提高，可作为临床辅助诊断的重要工具。

Abstract:: In order to improve the accuracy and efficiency of benign and malignant classification of mediastinal lymph nodes, We used the residual network (ResNet) and combined with the transfer learning method to design an automatic classification algorithm for benign and malignant mediastinal lymph nodes. The experimental results showed that the classification accuracy, sensitivity and specificity of this method reached 75.1%, 78.6% and 84.4% respectively, which were 6%, 9.9% and 15.4% higher than that of the traditional methods. The performance of the automatic mediastinal lymph node classification algorithm designed in this study is significantly higher than that of traditional methods. It can be used as an important tool for clinical auxiliary diagnosis.