X-ray and CT-scan-based automated detection and classification of covid-19 using convolutional neural networks (CNN)

Abstract

Covid-19 (Coronavirus Disease-2019) is the most recent coronavirus-related disease that has been announced as a pandemic by the World Health Organization (WHO). Furthermore, it has brought the whole planet to a halt as a result of the worldwide introduction of lockdown and killed millions of people. While this virus has a low fatality rate, the problem is that it is highly infectious, and as a result, it has infected a large number of people, putting a strain on the healthcare system, hence, Covid-19 identification in patients has become critical. The goal of this research is to use X-rays images and computed tomography(CT) images to introduce a deep learning strategy based on the Convolutional Neural Network (CNN) to automatically detect and identify the Covid-19 disease. We have implemented two different classifications using CNN(binary classification and multiclass classification). A total of 3,877 image datasets from CTs and X-rays were utilised to train the model in binary classification, with 1,917 images from Covid-19 infected individuals among them. The experimental results for binary classification show an overall accuracy of 99.64%, recall(or sensitivity) of 99.58%, the precision of 99.56%, F1-score of 99.59%, and ROC of 100%. For multiple classifications, the model was trained using a total of 6,077 images, with 1,917 images of Covid-19 infected people, 1,960 images of normal healthy people, and 2,200 images of pneumonia infected people. The experimental results for multiple classifications show an accuracy of 98.28%, recall(or sensitivity) of 98.25%, the precision of 98.22%, F1-score of 98.23%, and ROC of 99.87%. On the currently available dataset, the model produced the desired results, and it can assist healthcare workers in quickly detecting Covid-19 positive patients.

Learning-to-augment strategy using noisy and denoised data: Improving generalizability of deep CNN for the detection of COVID-19 in X-ray images

“…Refs. [ [12] , [13] , [14] ]). On the other hand, X-ray images are stored and transmitted in the form of compressed data [ 32 ].…”

“…It is reportedly used for COVID-19 detection in countries with a shortage of testing kits [ [6] , [7] , [8] ]. Recent studies [ [9] , [10] , [11] , [12] , [13] , [14] ] using machine learning (ML) and deep learning (DL) have shown promising results in the diagnosis of COVID-19. For example, convolutional neural networks (CNNs) have been applied for the classification of X-ray images [ [9] , [10] , [11] ] among COVID-19, non-COVID pneumonia (e.g., bacterial and viral pneumonia) and healthy cases.…”

COVID-19 Detection Based on Lung Ct Scan Using Deep Learning Techniques

“…By using these instances, the classifier was tested on a total of 6,077 images for different classifications. The multiclass has a 99.87% ROC, 98.2% accuracy, 98.25% recall, 98.22% F 1-score, 98.22% precision, and 98.22% precision [ 18 ]. The VGG16 and ResNet50 models are improved and optimized using augmentation of data and fine-tuning strategies.…”

Concatenation of Pre-Trained Convolutional Neural Networks for Enhanced COVID-19 Screening Using Transfer Learning Technique

“…Other research presented in [44], proposes a DL model based on CNN to identify the Coronavirus disease. The study reports a classification accuracy of 99.64% and 98.28% for binary and multiclass classification, respectively.…”