COVID-19 Detection Based on 6-Layered Explainable Customized Convolutional Neural Network

作     者：Jiaji Wang Shuwen Chen Yu Cao Huisheng Zhu Dimas Lima 

作者机构：School of Physics and Information EngineeringJiangsu Second Normal UniversityNanjing211200China State Key Laboratory of Millimeter WavesSoutheast UniversityNanjing210096China Jiangsu Province Engineering Research Center of Basic Education Big Data ApplicationNanjing211200China Department of Electrical EngineeringFederal University of Santa CatarinaFlorianópolis88040-900Brazil 

出 版 物：《Computer Modeling in Engineering & Sciences》 (工程与科学中的计算机建模（英文）)

年 卷 期：2023年第136卷第9期

页      面：2595-2616页

核心收录：

学科分类：12[管理学] 08[工学] 0831[工学-生物医学工程（可授工学、理学、医学学位）] 0710[理学-生物学] 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] 1001[医学-基础医学(可授医学、理学学位)] 081104[工学-模式识别与智能系统] 100201[医学-内科学(含：心血管病、血液病、呼吸系病、消化系病、内分泌与代谢病、肾病、风湿病、传染病)] 0805[工学-材料科学与工程（可授工学、理学学位）] 0835[工学-软件工程] 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）] 0702[理学-物理学] 10[医学] 

基　　金：supported partly by the Open Project of State Key Laboratory of Millimeter Wave under Grant K202218 partly by Innovation and Entrepreneurship Training Program of College Students under Grants 202210700006Y and 202210700005Z 

主　　题：COVID-19 custom convolutional neural network medical images 

摘      要：This paper presents a 6-layer customized convolutional neural network model(6L-CNN)to rapidly screen out patients with COVID-19 infection in chest CT *** model can effectively detect whether the target CT image contains images of pneumonia *** this method,6L-CNN was trained as a binary classifier using the dataset containing CT images of the lung with and without pneumonia as a *** results show that the model improves the accuracy of screening out COVID-19 *** to othermethods,the performance is *** addition,the method can be extended to other similar clinical conditions.