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      PulDi-COVID: Chronic Obstructive Pulmonary (Lung) Diseases With COVID-19 Classification Using Ensemble Deep Convolutional Neural Network From Chest X-Ray Images To Minimize Severity And Mortality Rates

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          Abstract

          Background

          and Objectiv

          In the current COVID-19 outbreak, efficient testing of COVID-19 individuals has proven vital to limiting and arresting the disease's accelerated spread globally. It has been observed that the severity and mortality ratio of COVID-19 affected patients is at greater risk because of chronic pulmonary diseases. This study looks at radiographic examinations exploiting chest X-ray images (CXI), which have become one of the utmost feasible assessment approaches for pulmonary disorders, including COVID-19. Deep Learning(DL) remains an excellent image classification method and framework; research has been conducted to predict pulmonary diseases with COVID-19 instances by developing DL classifiers with nine class CXI. However, a few claim to have strong prediction results; because of noisy and small data, their recommended DL strategies may suffer from significant deviation and generality failures.

          Methods

          Therefore, a unique CNN model(PulDi-COVID) for detecting nine diseases (atelectasis, bacterial-pneumonia, cardiomegaly, covid19, effusion, infiltration, no-finding, pneumothorax, viral-Pneumonia) using CXI has been proposed using the SSE algorithm. Several tranfer-learning models: VGG16, ResNet50, VGG19, DenseNet201, MobileNetV2, NASNetMobile, ResNet152V2, DenseNet169 are trained on CXI of chronic lung diseases and COVID-19 instances. Given that the proposed thirteen SSE ensemble models solved DL's constraints by making predictions with different classifiers rather than a single, we present PulDi-COVID, an ensemble DL model that combines DL with ensemble learning. The PulDi-COVID framework is created by incorporating various snapshots of DL models, which have spearheaded chronic lung diseases with COVID-19 cases identification process with a deep neural network produced CXI by applying a suggested SSE method. That is familiar with the idea of various DL perceptions on different classes.

          Results

          PulDi-COVID findings were compared to thirteen existing studies for nine-class classification using COVID-19. Test results reveal that PulDi-COVID offers impressive outcomes for chronic diseases with COVID-19 identification with a 99.70% accuracy, 98.68% precision, 98.67% recall, 98.67% F1 score, lowest 12 CXIs zero-one loss, 99.24% AUC-ROC score, and lowest 1.33% error rate. Overall test results are superior to the existing Convolutional Neural Network(CNN). To the best of our knowledge, the observed results for nine-class classification are significantly superior to the state-of-the-art approaches employed for COVID-19 detection. Furthermore, the CXI that we used to assess our algorithm is one of the larger datasets for COVID detection with pulmonary diseases.

          Conclusion

          The empirical findings of our suggested approach PulDi-COVID show that it outperforms previously developed methods. The suggested SSE method with PulDi-COVID can effectively fulfill the COVID-19 speedy detection needs with different lung diseases for physicians to minimize patient severity and mortality.

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          Most cited references24

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          Automated detection of COVID-19 cases using deep neural networks with X-ray images

          The novel coronavirus 2019 (COVID-2019), which first appeared in Wuhan city of China in December 2019, spread rapidly around the world and became a pandemic. It has caused a devastating effect on both daily lives, public health, and the global economy. It is critical to detect the positive cases as early as possible so as to prevent the further spread of this epidemic and to quickly treat affected patients. The need for auxiliary diagnostic tools has increased as there are no accurate automated toolkits available. Recent findings obtained using radiology imaging techniques suggest that such images contain salient information about the COVID-19 virus. Application of advanced artificial intelligence (AI) techniques coupled with radiological imaging can be helpful for the accurate detection of this disease, and can also be assistive to overcome the problem of a lack of specialized physicians in remote villages. In this study, a new model for automatic COVID-19 detection using raw chest X-ray images is presented. The proposed model is developed to provide accurate diagnostics for binary classification (COVID vs. No-Findings) and multi-class classification (COVID vs. No-Findings vs. Pneumonia). Our model produced a classification accuracy of 98.08% for binary classes and 87.02% for multi-class cases. The DarkNet model was used in our study as a classifier for the you only look once (YOLO) real time object detection system. We implemented 17 convolutional layers and introduced different filtering on each layer. Our model (available at (https://github.com/muhammedtalo/COVID-19)) can be employed to assist radiologists in validating their initial screening, and can also be employed via cloud to immediately screen patients.
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            Real-time RT-PCR in COVID-19 detection: issues affecting the results

            Due to the rapid spread and increasing number of coronavirus disease 19 (COVID-19) cases caused by a new coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), rapid and accurate detection of virus and/or disease is increasingly vital to control the sources of infection and help patients to prevent the illness progression. Since December 2019, there has been considerable challenge regarding the use of nucleic acid test or clinical characteristics of infected patients as the reference standard to make a definitive diagnose of COVID-19 patients. As the early diagnosis of COVID-19 is critical for prevention and control of this pandemic, clinical characteristics cannot alone define the diagnosis of COVID-19, especially for patients presenting early-onset of symptoms. Along with the advancement in medical diagnosis, nucleic acid detection-based approaches have become a rapid and reliable technology for viral detection. Among nucleic acid tests, the polymerase chain reaction (PCR) method is considered as the ‘gold standard’ for the detection of some viruses and is characterized by rapid detection, high sensitivity, and specificity. As such, real-time reverse transcriptase-PCR (RT-PCR) is of great interest today for the detection of SARS-CoV-2 due to its benefits as a specific and simple qualitative assay [1–3]. Moreover, real-time RT-PCR has adequate sensitivity to help us much for diagnosing early infection. Therefore, the ‘criterion-referenced’ real-time RT-PCR assay can be considered as a main method to be applied to detect the causative agent of COVID-19, SARS-CoV-2. An important issue with the real-time RT-PCR test is the risk of eliciting false-negative and false-positive results. It is reported that many ‘suspected’ cases with typical clinical characteristics of COVID-19 and identical specific computed tomography (CT) images were not diagnosed [4]. Thus, a negative result does not exclude the possibility of COVID-19 infection and should not be used as the only criterion for treatment or patient management decisions. It seems that combination of real-time RT-PCR and clinical features facilitates management of SARS-CoV-2 outbreak. Several factors have been proposed to be associated with the inconsistency of real-time RT-PCR [5]. In the following, we attempt to discuss various challenges regarding the detection of SARS-CoV-2 by real-time RT-PCR. It is expected that this could provide beneficial information for the comprehension of the limitations of the obtained results and to improve diagnosis approaches and control of the disease. It is well known that results from real-time RT-PCR using primers in different genes can be affected by the variation of viral RNA sequences. Genetic diversity and rapid evolution of this novel coronavirus have been observed in different studies [6,7]. False-negative results may occur by mutations in the primer and probe target regions in the SARS-CoV-2 genome. Although it was attempted to design the real-time RT-PCR assay as precisely as possible based on the conserved regions of the viral genomes, variability causing mismatches between the primers and probes and the target sequences can lead to decrease in assay performance and potential false-negative results. In this regard, multiple target gene amplification could be used to avoid invalid results. Several types of SARS-CoV-2 real-time RT-PCR kit have been developed and approved rapidly, but with different quality. Importantly, the sensitivity and specificity of the real-time RT-PCR test is not 100%. All of them behind the laboratory practice standard and personnel skill in the relevant technical and safety procedures explain some of the false-negative results. According to the natural history of the COVID-19 and viral load kinetics in different anatomic sites of the patients, sampling procedures largely contribute to the false-negative results. Optimum sample types and timing for peak viral load during infections caused by SARS-CoV-2 remain to be fully determined. A study has reported sputum as the most accurate sample for laboratory diagnosis of COVID-19, followed by nasal swabs, while throat swabs were not recommended for the diagnosis [8]. They also suggested the detection of viral RNAs in bronchoalveolar lavage fluid (BALF) for the diagnosis and monitoring of viruses in severe cases. However, gathering of BALF needs both a suction tool and an expert operator, in addition to being painful to the patients. While BALF samples are not practical for the routine laboratory diagnosis and monitoring of the disease, collection of other samples such as sputum, nasal swab, and throat swab is rapid, simple, and safe. To avoid inconsistent results, it would be better to use different specimen types (stool and blood) besides respiratory specimen during different stages. It is worth noting that samples should be obtained by dacron or polyester flocked swabs and should reach the laboratory as soon as possible after collection. False-negative results may occur due to the presence of amplification inhibitors in the sample or insufficient organisms in the sample rising from inappropriate collection, transportation, or handling. Viral load kinetics of SARS-CoV-2 infection have been described in two patients in Korea, suggesting a different viral load kinetics from that of previously reported other coronavirus infections [9]. In the first patient, the virus was detected from upper respiratory tract (URT) and lower respiratory tract (LRT) specimens on days 2 and 3 of symptom onset, respectively. On day 5, the viral load was increased from day 3 in the LRT specimen. However, the viral loads decreased from around day 7 in both URT and LRT specimens. Real-time RT-PCR continued to be positive at a low level until day 13 (LRT specimens) and day 14 (URT specimens). Finally, the assay became undetectable for two consecutive days from day 14 (LRT specimen) and day 15 (URT specimen), respectively. In the second patient, SARS-CoV-2 was detected in both URT and LRT specimens on day 14 of symptom onset. However, the initial viral loads were relatively lower than those of patient 1 in whom the test was performed on day 2 of symptom onset. From day 18 (URT specimen) and day 20 (LRT specimen), real-time RT-PCR became undetectable for two consecutive days, respectively. URT sample of day 25 was again positive for RdRp and E genes. However, it was interpreted as negative due to high Ct value of the RdRp gene (Ct value of 36.69). These findings indicate the different viral load kinetics of SARS-coV-2 in different patients, suggesting that sampling timing and period of the disease development play an important role in real-time RT-PCR results. Finally, the Centers for Disease Control and Prevention (CDC) has designed a SARS-CoV-2 Real-Time RT-PCR Diagnostic Panel to minimize the chance of false-positive results [10]. In accordance, the negative template control (NTC) sample should be negative, showing no fluorescence growth curves that cross the threshold line. The occurrence of false positive with one or more of the primer and probe NTC reactions is indicative of sample contamination. Importantly, the internal control should be included to help identify the specimens containing substances that may interfere with the extraction of nucleic acid and PCR amplification. Because of the several risks to patients in the event of a false-positive result, all clinical laboratories using this test must follow the standard confirmatory testing and reporting guidelines based on their proper public health authorities. 1. Expert opinion In conclusion, according to the mentioned reasons, the results of real-time RT-PCR tests must be cautiously interpreted. In the case of real-time RT-PCR negative result with clinical features suspicion for COVID-19, especially when only upper respiratory tract samples were tested, multiple sample types in different time points, including from the lower respiratory tract if possible, should be tested. Importantly, combination of real-time RT-PCR and clinical features especially CT image could facilitate disease management. Proper sampling procedures, good laboratory practice standard, and using high-quality extraction and real-time RT-PCR kit could improve the approach and reduce inaccurate results.
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              Is Open Access

              COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images

              The Coronavirus Disease 2019 (COVID-19) pandemic continues to have a devastating effect on the health and well-being of the global population. A critical step in the fight against COVID-19 is effective screening of infected patients, with one of the key screening approaches being radiology examination using chest radiography. It was found in early studies that patients present abnormalities in chest radiography images that are characteristic of those infected with COVID-19. Motivated by this and inspired by the open source efforts of the research community, in this study we introduce COVID-Net, a deep convolutional neural network design tailored for the detection of COVID-19 cases from chest X-ray (CXR) images that is open source and available to the general public. To the best of the authors’ knowledge, COVID-Net is one of the first open source network designs for COVID-19 detection from CXR images at the time of initial release. We also introduce COVIDx, an open access benchmark dataset that we generated comprising of 13,975 CXR images across 13,870 patient patient cases, with the largest number of publicly available COVID-19 positive cases to the best of the authors’ knowledge. Furthermore, we investigate how COVID-Net makes predictions using an explainability method in an attempt to not only gain deeper insights into critical factors associated with COVID cases, which can aid clinicians in improved screening, but also audit COVID-Net in a responsible and transparent manner to validate that it is making decisions based on relevant information from the CXR images. By no means a production-ready solution, the hope is that the open access COVID-Net, along with the description on constructing the open source COVIDx dataset, will be leveraged and build upon by both researchers and citizen data scientists alike to accelerate the development of highly accurate yet practical deep learning solutions for detecting COVID-19 cases and accelerate treatment of those who need it the most.
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                Author and article information

                Journal
                Biomed Signal Process Control
                Biomed Signal Process Control
                Biomedical Signal Processing and Control
                Elsevier Ltd.
                1746-8094
                1746-8094
                30 November 2022
                30 November 2022
                : 104445
                Affiliations
                Department of Computer Science and Engineering, Birla Institute of Technology, Mesra, Ranchi 835215, India
                Author notes
                [* ]Corresponding author.
                Article
                S1746-8094(22)00899-0 104445
                10.1016/j.bspc.2022.104445
                9708623
                36466567
                473108cb-047e-482c-a710-d9c4eb94b9da
                © 2022 Elsevier Ltd. All rights reserved.

                Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.

                History
                : 17 April 2022
                : 10 October 2022
                : 20 November 2022
                Categories
                Article

                biomedical engineering,convolution neural networks (cnn),ensemble deep learning,chronic pulmonary disease,covid-19,diagnosis & classification,transfer learning

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