An Inception Module CNN Classifiers Fusion Method on Pulmonary Nodule Diagnosis by Signs

• 来源期刊: 清华大学学报自然科学版（英文版）
• 年，卷（期）: 2020,（3）
• 页码范围: 368-383
• 页数: 16页
• 语种: 英文
• DOI: 10.26599/TST.2019.9010010

摘要

A "sign" on a lung CT image refers to a radiologic finding that suggests a pathological progression of some specific disease.Analysis of CT signs is helpful to understand the pathological origin of the lesion.In-depth study of lung nodules classification with different CT signs will help to distinguish benign and malignant nodules more clearly and accurately.To this end,we propose an Inception module-based ensemble classification method for pulmonary nodule diagnosis with different nodule signs.We first construct a Convolutional Neural Network (CNN) classifier adopting Inception modules and pre-train it on ImageNet.We then fine-tune this pre-trained classifier on 10 different lung nodule sign sample sets,and fuse these 10 classifiers with an artificial immune ensemble algorithm.The overall sensitivity,specificity,and accuracy of our proposed Artificial Immune Algorithm-based Inception Networks Fusion (AIA-INF) algorithm are 82.22％,93.17％,and 88.67％,respectively,which are significantly higher than those of the alternative Bagging and Boosting methods.The experimental results show that our Inception-based ensemble classifier offers promising performance,and compared with other CADx systems,this scheme can offer a more detailed reference for diagnosis,and can be valuable for junior radiologist training.