A deep learning approach to detect Covid-19 coronavirus with X-Ray images

Abstract

Rapid and accurate detection of COVID-19 coronavirus is necessity of time to prevent and control of this pandemic by timely quarantine and medical treatment in absence of any vaccine. Daily increase in cases of COVID-19 patients worldwide and limited number of available detection kits pose difficulty in identifying the presence of disease. Therefore, at this point of time, necessity arises to look for other alternatives. Among already existing, widely available and low-cost resources, X-ray is frequently used imaging modality and on the other hand, deep learning techniques have achieved state-of-the-art performances in computer-aided medical diagnosis. Therefore, an alternative diagnostic tool to detect COVID-19 cases utilizing available resources and advanced deep learning techniques is proposed in this work. The proposed method is implemented in four phases, viz., data augmentation, preprocessing, stage-I and stage-II deep network model designing. This study is performed with online available resources of 1215 images and further strengthen by utilizing data augmentation techniques to provide better generalization of the model and to prevent the model overfitting by increasing the overall length of dataset to 1832 images. Deep network implementation in two stages is designed to differentiate COVID-19 induced pneumonia from healthy cases, bacterial and other virus induced pneumonia on X-ray images of chest. Comprehensive evaluations have been performed to demonstrate the effectiveness of the proposed method with both (i) training-validation-testing and (ii) 5-fold cross validation procedures. High classification accuracy as 97.77%, recall as 97.14% and precision as 97.14% in case of COVID-19 detection shows the efficacy of proposed method in present need of time. Further, the deep network architecture showing averaged accuracy/sensitivity/specificity/precision/F1-score of 98.93/98.93/98.66/96.39/98.15 with 5-fold cross validation makes a promising outcome in COVID-19 detection using X-ray images.

Deep learning model to predict the need for mechanical ventilation using chest X-ray images in hospitalised patients with COVID-19

“…Previously, the CheXNeXT system 10 has been used to predict 14 pathologies based on chest X-rays but not in the context of COVID-19 infection. There have been several studies [15][16][17][18][19] to detect or diagnose COVID-19 based on chest radiographs, but deep learning attempts to prognosticate COVID-19 disease course have been few and far between. Cohen et al 20 developed an algorithm to predict patients at high risk of mortality; Zhu et al 21 have developed a deep learning method to stage disease severity, the CheXNeXt deep learning; and, recently, Li et al 22 have developed a deep-learning Siamese network to predict the Radiographic Assessment of Lung Edema (RALE) scores 23 used to quantify severity of ARDS in patients with COVID-19.…”

COVID-19 X-ray images classification based on enhanced fractional-order cuckoo search optimizer using heavy-tailed distributions

“…Furthermore, Jain et al. [54] applied a deep learning model based on ResNet-101 for COVID-19 X-ray image classification. The proposed model achieved significant performance using several performance measures.…”

These do not Look Like Those: An Interpretable Deep Learning Model for Image Recognition

“…This type of human reasoning is accommodated by the ProtoPNet model, where comparison of image parts with learned prototypes is integral to the reasoning process of the model. Recently, some deep learning/machine learning models have been developed to classify the X-ray images of Covid-19 patients, normal people and pneumonia patients, see [1], [7], [16], [17], [19], [25], [28], [44]. A survey article is also written that summarizes the research works related to deep learning applications on COVID-19 medical image processing [2].…”