Stacked Convolutional Neural Network for Diagnosis of COVID-19 Disease from X-ray Images

2020/06/22 by Mahesh Gour, Gour, Mahesh, Sweta Jain +1 · 34 citations
Computer Science · Engineering · Medicine · #AI in cancer detection #Artificial intelligence #COVID-19 diagnosis using AI #Computer Vision and Pattern Recognition (cs.CV) #Computer science #Convolutional neural network #Coronavirus disease 2019 (COVID-19) #Deep learning #Disease #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Internal medicine #Logistic regression #Machine Learning (cs.LG) #Machine learning #Medicine #Pathology #Pattern recognition (psychology) #Pneumonia #Radiomics and Machine Learning in Medical Imaging #Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) #cs.CV #cs.LG #eess.IV #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2006.13817

published in arXiv (Cornell University) (Cornell University) · 6 tables, 4 figures

arxiv created 2020/06/22 · openalex publication_date 2020/06/22 · arxiv updated 2020/06/25 · openalex created_date 2020/07/02 · openalex updated_date 2026/07/28

Abstract

Automatic and rapid screening of COVID-19 from the chest X-ray images has become an urgent need in this pandemic situation of SARS-CoV-2 worldwide in 2020. However, accurate and reliable screening of patients is a massive challenge due to the discrepancy between COVID-19 and other viral pneumonia in X-ray images. In this paper, we design a new stacked convolutional neural network model for the automatic diagnosis of COVID-19 disease from the chest X-ray images. We obtain different sub-models from the VGG19 and developed a 30-layered CNN model (named as CovNet30) during the training, and obtained sub-models are stacked together using logistic regression. The proposed CNN model combines the discriminating power of the different CNN`s sub-models and classifies chest X-ray images into COVID-19, Normal, and Pneumonia classes. In addition, we generate X-ray images dataset referred to as COVID19CXr, which includes 2764 chest x-ray images of 1768 patients from the three publicly available data repositories. The proposed stacked CNN achieves an accuracy of 92.74%, the sensitivity of 93.33%, PPV of 92.13%, and F1-score of 0.93 for the classification of X-ray images. Our proposed approach shows its superiority over the existing methods for the diagnosis of the COVID-19 from the X-ray images.

Citations

• Adam: A Method for Stochastic Optimization
• Very Deep Convolutional Networks for Large-Scale Image Recognition
• ImageNet classification with deep convolutional neural networks
• Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China
• Serial interval of novel coronavirus (COVID-19) infections
• Weakly Supervised Deep Learning for COVID-19 Infection Detection and Classification from CT Images
• COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images

Related

• Randomly Initialized Convolutional Neural Network for the Recognition of COVID-19 using X-ray Images
• Recognition of COVID-19 Disease Utilizing X-Ray Imaging of the Chest Using CNN
• A design of Convolutional Neural Network model for the Diagnosis of the COVID-19
• Automatic Detection and Classification of Corona Infection (COVID-19) from X-ray Images Using Convolution Neural Network
• A Deep Learning Approach for the Detection of COVID-19 from Chest X-Ray Images using Convolutional Neural Networks