Convid-Net: An Enhanced Convolutional Neural Network Framework for COVID-19 Detection from X-Ray Images

This article aims to demonstrate a deep convolutional neural network (CNN) framework namely Convid-Net based on a combination of residual network and parallel convolution (CONV) to detect COVID-19 from chest X-ray images The proposed architecture can choose optimum features from different parallel CONV and residual connection increasing overall accuracy with less computational expenses A custom dataset has been created for this work which consists of total 1440 images of COVID-19, 2470 normal images and 2407 chest X-ray images of viral and bacterial pneumonia;collected from different publicly available sources Augmentation and preprocessing have been applied as well to increase the number of data for better training purposes Convid-Net has been trained and tested on a prepared augmented dataset which achieved accuracy of 97 99% The promising result of the proposed system shows that it converges to an overall higher accuracy and can be a very useful method for physicians and radiologists to assist them in rapid detection and diagnosis of COVID-19 from radiography images These results also indicate that Convid-Net architecture can further be used in other image based classification tasks © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd