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

作     者：Guangyuan Zheng Guanghui Han Nouman Qadeer Soomro Guangyuan Zheng;Guanghui Han;Nouman Qadeer Soomro

作者机构：the Beijing Key Laboratory of Intelligent Information TechnologySchool of Computer Science and TechnologyBeijing Institute of TechnologyBeijing 100081China the School of Information TechnologyShangqiu Normal UniversityShangqiu 476000China the School of Biomedical EngineeringSun Yat-sen UniversityGuangzhou 510006China the Department of Software EngineeringMehran University of Engineering and TechnologySZAB CampusKhairpur Mir's66020Pakistan 

出 版 物：《Tsinghua Science and Technology》 (清华大学学报（自然科学版（英文版）)

年 卷 期：2020年第25卷第3期

页      面：368-383页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 1002[医学-临床医学] 081104[工学-模式识别与智能系统] 08[工学] 080203[工学-机械设计及理论] 0835[工学-软件工程] 0802[工学-机械工程] 100214[医学-肿瘤学] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 10[医学] 

基　　金：Government of Pakistan Japan Science and Technology Agency, JST 

主　　题：sign lung cancer pulmonary nodule Convolutional Neural Network(CNN) Artificial Immune Algorithm(AIA) 

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.