Coronavirus Detection Using Two Step-AS Clustering and Ensemble Neural Network Model  

作　　者：Ahmed Hamza Osman 

机构地区：[1]Department of Information System,Faculty of Computing and Information Technology King Abdulaziz University Rabigh,Saudi Arabia

出　　处：《Computers, Materials & Continua》2022年第6期6307-6331,共25页计算机、材料和连续体（英文）

基　　金：This work was funded by the Deanship of Scientific Research(DSR)at King Abdulaziz University,Jeddah,Saudi Arabia,under Grant No.(DF-770830-1441);The author,there-fore,gratefully acknowledge the technical and financial support from the DSR.

摘　　要：This study presents a model of computer-aided intelligence capable of automatically detecting positive COVID-19 instances for use in regular medical applications.The proposed model is based on an Ensemble boosting Neural Network architecture and can automatically detect discriminatory features on chestX-ray images through Two Step-As clustering algorithm with rich filter families,abstraction and weight-sharing properties.In contrast to the generally used transformational learning approach,the proposed model was trained before and after clustering.The compilation procedure divides the datasets samples and categories into numerous sub-samples and subcategories and then assigns new group labels to each new group,with each subject group displayed as a distinct category.The retrieved characteristics discriminant cases were used to feed the Multiple Neural Network method,which was then utilised to classify the instances.The Two Step-AS clustering method has been modified by pre-aggregating the dataset before applying Multiple Neural Network algorithm to detect COVID-19 cases from chest X-ray findings.Models forMultiple Neural Network and Two Step-As clustering algorithms were optimised by utilising Ensemble Bootstrap Aggregating algorithm to reduce the number of hyper parameters they include.The testswere carried out using theCOVID-19 public radiology database,and a cross-validationmethod ensured accuracy.The proposed classifier with an accuracy of 98.02%percent was found to provide the most efficient outcomes possible.The result is a lowcost,quick and reliable intelligence tool for detecting COVID-19 infection.

关 键 词：Two step-AS clustering ensemble learning bootstrap aggregating multiple neural network covid-19 X-ray images 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]