Ensemble learning-based COVID-19 detection by feature boosting in chest X-ray images

作     者：Upadhyay, Kamini Agrawal, Monika Deepak, Desh 

作者机构：IIT Delhi Ctr Appl Res Elect New Delhi India Dr Ram Manohar Lohia Hosp Dept Resp Med New Delhi India 

出 版 物：《IET IMAGE PROCESSING》 (IET影像处理)

年 卷 期：2020年第14卷第16期

页      面：4059-4066页

核心收录：

学科分类：0808[工学-电气工程] 1002[医学-临床医学] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：diseases patient diagnosis learning (artificial intelligence) regression analysis diagnostic radiography medical image processing image fusion COVID-19 patients pre-trained deep models base-learners logistic regression model meta learner base-learner predictions fusion-based model sensitivity values ensemble learning-based COVID-19 detection chest X-ray images novel coronavirus current testing rate exponential rate available testing methodologies sensitive automated diagnosis coronavirus disease COVID-specific features 

摘      要：The novel coronavirus has spread quite rapidly across the globe. The current testing rate is failing to match the exponential rate of rising cases. Moreover, the available testing methodologies are expensive and time-consuming. A sensitive automated diagnosis is one of the biggest need of the hour. In the proposed work, the authors analyse the chest X-ray images of normal, pneumonia and coronavirus disease-2019 (COVID-19) patients and process them to boost the COVID-specific features (opacities etc.), which enable to perform sensitive identification of COVID-19 patients. The sets of original and processed images are used with a stack of pre-trained deep models for ensemble learning. They used VGG-16 as base-learners, trained with a diverse set of inputs followed by a logistic regression model, the meta learner, to combine the base-learner predictions. The proposed fusion-based model is trained and tested for three types of classification, TYPE-I: binary (NORMAL/ABNORMAL), TYPE-II: binary (PNEUMONIA/COVID-19) and TYPE-III: multi-class (NORMAL/PNEUMONIA/COVID-19). The diagnosis results are quite promising, with high accuracy and sensitivity values for all the cases. The proposed algorithm can be used to assist the medical experts for quick identification and isolation of COVID-19 patients and thereby mitigating the effect of the virus.