Severe Acute Respiratory Syndrome 2 (SARS-CoV-2), the name given by the International Committee on Taxonomy of Viruses (ICTV), instigates the novel coronavirus or COVID-19. Recently COVID-19 has become a global health emergency that has appeared in Wuhan, China in late December 2019. Coronavirus is one of the significant pathogens that fundamentally focus on the human respiratory framework and is highly infectious to the health of individuals. The virus later transformed into a worldwide pandemic, as declared by World Health Organization (WHO).1 In the primary stage of disease transmission, the number of people affected by the disease was minimum; it did not imitate threats of such huge capacity.2 With the slow progression of time, the virus spread with an extremely high-risk potential of affecting millions of lives in all countries, and become the principal factor of many people’s death globally.3 Due to high mortality rate by COVID-19 cases worldwide, countries with more number of active cases suggest people to stay indoors and announced a complete lockdown to stop disease transmission.4 COVID-19 infection is fatal because it is easily transmitted through direct or indirect contact with the affected individual and its symptoms include fever, dry cough, vomiting, diarrhea, and myalgia as indicated by WHO and Centre for Disease and Control (CDC). Till date, May 2021, the COVID-19 pandemic already contributed to over 3 170 882 deaths and more than 150 779 711 confirmed cases of COVID-19 infection.5 Researchers are actively participating to detect the COVID-19 positive cases and also finding the diagnosis procedure and medical treatment of affected patients rapidly. Daily increment of positive COVID-19 cases and wrong diagnosis is challenging in managing the pandemic. With large number of infected individuals and less number of test kits, practitioners totally rely upon automated detection system to combat the pandemic proficiently at early stage. These detection systems could also be important in identifying patients who need isolation to prevent the disease from spreading in the community. The laboratory tests used for COVID-19 detection including nucleic acid reagent detection, viral antigen detection were time-consuming and produce a higher false-negative detection rate.
Reverse transcription-polymerase chain reaction (RT-PCR) test-kits have emerged as the main technique for diagnosing COVID-19. The current RT-PCR system is time-consuming and also it requires additional resources and approval to detect infected patients, which can be more costly in many developing societies. Due to the inaccessibility of test kits and the false-negative rate of virus and antibody tests, the medical authorities have temporarily used radiological examinations as a clinical investigation for COVID-19 detection but there are limited kits for detecting the virus efficiently.6 These issues have posed a great life threat, especially in societies with limited medical assets. However, noting the recent spread of COVID-19, the researchers need to look for other better options like X-ray and computed tomography (CT) scans. The information obtained from radiological images, that is, X-ray and CT scans is important for clinical diagnosis. Radiological images have high sensitivity in disease diagnosis and more accurate than RT-PCR. Thus, radiologists can notice the characteristics of a lung infected with COVID-19 by using chest X-ray and CT scan.7 CT scan causes multiple problems of healthcare when requiring multiple scans during the course of disease. The American College of radiology disapproves the use of CT scan as a first line of diagnosis. So the medical practitioners recommended chest X-ray (CXR) than CT scan radiography.8 CXR requires less expensive equipment’s and also can be used in an isolated room and prevent the risk of infection to other persons.9, 10 Therefore, it is worth combining these radiographic images with artificial intelligent (AI) system for better and accurate predictions of COVID-19.11 Machine learning (ML) and deep learning (DL) were the two sub-categories of AI that have been used for detection of various diseases.12 The image processing technology has gained immense momentum in all sectors of healthcare, especially in the field of lung disease detection.13, 14 Hence these methods have been a natural choice for COVID-19 research as well. Other testing methods namely chest X-ray, CT scans, are also being considered by many nations to aid diagnosis and provide evidence of more serious disease progression.6, 15, 16 Inspired by this, several investigators and sources recommended the use of chest radiographs for detection of COVID-19.17, 18 Thus, radiologists can notice the characteristics of a lung infected with COVID-19 by using radiographic data.19 Several applications using DL approaches have already been proposed as an attempt to address COVID-19 detection from chest X-ray.20 As various studies cited in the related work section revealed that chest X-ray images have the potential to monitor and examine various lung diseases such as tuberculosis, infiltration, atelectasis, pneumonia, and hernia. Also chest X-ray diagnosis systems are cheap and widely available. COVID-19, which shows as an upper respiratory tract and lung infection can also be detected by using chest X-ray imaging modality. The present study focuses on using different DL methods to combat the COVID-19 pandemic using an automated detection system for accurate and fast decision making, as self or manual reading of chest radiological (X-ray) data of infected patients take a significant time. Our research coined with DL ensemble model to solve binary class (COVID-19 vs. non-COVID) and multiclass (COVID-19 vs. pneumonia vs. non-COVID) problems. The proposed ensemble model uses various pre-trained deep neural networks for deep feature extraction. These DL algorithms previously employed in many image classification and computer vision problems. After feature extraction, different ensemble models were used to produce the final prediction.
- We develop an ensemble learning based system using deep convolutional neural network which is trained and evaluated on publically available chest X-ray image dataset having the knowledge to classify between Covid vs. normal vs. pneumonia subjects. The limited number of COVID-19 subjects used by many researchers in their work21–23 leads to degrading the model efficiency as with lower COVID-19 subjects, the severity of the disease is not properly diagnosed. A large medical image dataset is used in our work and the proposed model shows promising results in terms of accuracy and other evaluation metrics (precision, recall, and F-1 score).
- We have used four (Inceptionv3, DenseNet121, Xception, and InceptionResNetv2) powerful and efficient DL models with more number of training parameters that are coupled with global based features for classifying COVID-19 subjects from other classes and reduce the misdiagnosis rate for COVID-19 and help the doctors, field specialists, and physician to know the severity of disease at early stage.
- Data augmentation methods are employed for COVID-19 detection to avoid overfitting problems. We have fine-tuned our model by using four pre-trained models in depth to compensate for the loss of valuable information.
2 RELATED WORK
Recently, there have been numerous AI learning-based methods that were used for COVID-19 detection using radiographic data. In the medical care field, DL which is a sub-branch of AI is in effect and gradually developed as a Computer-Aided Design (CAD) tool to help doctors/radiological experts for better disease prediction.24 DL methods can be a guide for professionals to improve the quality of COVID-19 detection.21, 23, 25, 26 Already many DL methods have been applied in the healthcare field8, 27 to address a variety of issues such as COVID-19 detection using X-ray images and CT scans.9, 10, 23, 28–31 Also newly modified CNN methods have been proposed for COVID-19 detection such as COVID-Net,21 CoroNet,<a rel="nofollow" href="https://onlinelibrary.wiley.com/doi/full/10.1002/#ima22627-bib-0026" class="bibLink tab-link" id="ima22627-bib-0026R" data-tab="pane-pcw-references" aria-label="Reference 26 – Comput Methods Programs…