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

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.