Abstract
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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
WHO Coronavirus Disease (COVID-19) Dashboard. https://covid19.who.int
Wynants, L, et al.: Prediction models for diagnosis and prognosis of covid-19 infection: systematic review and critical appraisal. BMJ 369 (2020)
Litjens, G., Kooi, T., Bejnordi, B.E., Setio, A.A.A., Ciompi, F., Ghafoorian, M., Van Der Laak, J.A., Van Ginneken, B., Sánchez, C.I.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60–88 (2017)
Khan, A.I., Shah, J.L., Bhat, M.M.: Coronet: a deep neural network for detection and diagnosis of covid-19 from chest X-ray images. In: Computer Methods and Programs in Biomedicine, p. 105581 (2020)
Islam, M.Z., Islam, M.M., Asraf, A.: A combined deep CNN-LSTM network for the detection of novel coronavirus (covid-19) using X-ray images. In: Informatics in Medicine Unlocked, p. 100412 (2020)
Oh, Y., Park, S., Ye, J.C.: Deep learning covid-19 features on CXR using limited training data sets. IEEE Trans. Med. Imag. (2020)
Wang, L., Wong, A.: Covid-net: a tailored deep convolutional neural network design for detection of covid-19 cases from chest X-ray images. arXiv preprint arXiv:2003.09871 (2020)
Narin, A., Kaya, C., Pamuk, Z.: Automatic detection of coronavirus disease (covid-19) using X-ray images and deep convolutional neural networks. arXiv preprint arXiv:2003.10849 (2020)
Abbas, A., Abdelsamea, M.M., Gaber, M.M.: Classification of covid-19 in chest X-ray images using detrac deep convolutional neural network. arXiv preprint arXiv:2003.13815 (2020)
Ghoshal, B., Tucker, A.: Estimating uncertainty and interpretability in deep learning for coronavirus (covid-19) detection. arXiv preprint arXiv:2003.10769 (2020)
Khalifa, N.E.M., Taha, M.H.N., Hassanien, A.E., Elghamrawy, S.: Detection of coronavirus (covid-19) associated pneumonia based on generative adversarial networks and a fine-tuned deep transfer learning model using chest X-ray dataset. arXiv preprint arXiv:2004.01184 (2020)
Ozturk, S., Ozkaya, U., Barstugan, M.: Classification of coronavirus images using shrunken features. medRxiv (2020)
Shi, F., Wang, J., Shi, J., Wu, Z., Wang, Q., Tang, Z., He, K., Shi, Y., Shen, D.: Review of artificial intelligence techniques in imaging data acquisition, segmentation and diagnosis for covid-19. IEEE reviews in biomedical engineering (2020)
Chen, D., Ji, S., Liu, F., Li, Z., Zhou, X.: A review of automated diagnosis of covid-19 based on scanning images (2020)
COVID-19 X ray dataset (Train & test sets). https://kaggle.com/khoongweihao/covid19-xray-dataset-train-test-sets
COVID-19 patients lungs X ray images 10000. https://kaggle.com/nabeelsajid917/covid-19-x-ray-10000-images
COVID-19 radiography database. https://kaggle.com/tawsifurrahman/covid19-radiography-database
Cohen, J.P.: IEEE8023/covid-chestxray-dataset, Sept 2020. https://github.com/ieee8023/covid-chestxray-dataset, original-date: 2020-02-14T23:22:23Z
COVID-19 detection X-ray dataset. https://kaggle.com/darshan1504/covid19-detection-xray-dataset
Agchung: Agchung/Actualmed-COVID-chestxray-dataset, Sept 2020. https://github.com/agchung/Actualmed-COVID-chestxray-dataset, original-date: 2020-04-17T15:45:49Z
BIMCV Medical Imaging Databank of the Valencia Region, Pertusa, A., de la Iglesia Vaya, M.: BIMCV-COVID19+ (May 2020). OSF. https://doi.org/10.17605/OSF.IO/NH7G8. https://osf.io/nh7g8/
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Ahmed, S., Hossain, M.F., Noor, M.B.T. (2021). Convid-Net: An Enhanced Convolutional Neural Network Framework for COVID-19 Detection from X-Ray Images. In: Kaiser, M.S., Bandyopadhyay, A., Mahmud, M., Ray, K. (eds) Proceedings of International Conference on Trends in Computational and Cognitive Engineering. Advances in Intelligent Systems and Computing, vol 1309. Springer, Singapore. https://doi.org/10.1007/978-981-33-4673-4_55
Download citation
DOI: https://doi.org/10.1007/978-981-33-4673-4_55
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-33-4672-7
Online ISBN: 978-981-33-4673-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)