scholarly journals Detection of COVID-19 Patients from CT Scan and Chest X-ray Data Using Modified MobileNetV2 and LIME

Healthcare ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 1099
Author(s):  
Md Manjurul Ahsan ◽  
Redwan Nazim ◽  
Zahed Siddique ◽  
Pedro Huebner
Keyword(s):  
High Performance ◽  
Disease Diagnosis ◽  
The Novel ◽  
Deep Convolutional Neural Networks ◽  
X Ray ◽  
Patient Screening ◽  
Global Pandemic ◽  
Chest X Ray ◽  
Interpretable Model ◽  
Novel Coronavirus

The COVID-19 global pandemic caused by the widespread transmission of the novel coronavirus (SARS-CoV-2) has become one of modern history’s most challenging issues from a healthcare perspective. At its dawn, still without a vaccine, contagion containment strategies remained most effective in preventing the disease’s spread. Patient isolation has been primarily driven by the results of polymerase chain reaction (PCR) testing, but its initial reach was challenged by low availability and high cost, especially in developing countries. As a means of taking advantage of a preexisting infrastructure for respiratory disease diagnosis, researchers have proposed COVID-19 patient screening based on the results of Chest Computerized Tomography (CT) and Chest Radiographs (X-ray). When paired with artificial-intelligence- and deep-learning-based approaches for analysis, early studies have achieved a comparatively high accuracy in diagnosing the disease. Considering the opportunity to further explore these methods, we implement six different Deep Convolutional Neural Networks (Deep CNN) models—VGG16, MobileNetV2, InceptionResNetV2, ResNet50, ResNet101, and VGG19—and use a mixed dataset of CT and X-ray images to classify COVID-19 patients. Preliminary results showed that a modified MobileNetV2 model performs best with an accuracy of 95 ± 1.12% (AUC = 0.816). Notably, a high performance was also observed for the VGG16 model, outperforming several previously proposed models with an accuracy of 98.5 ± 1.19% on the X-ray dataset. Our findings are supported by recent works in the academic literature, which also uphold the higher performance of MobileNetV2 when X-ray, CT, and their mixed datasets are considered. Lastly, we further explain the process of feature extraction using Local Interpretable Model-Agnostic Explanations (LIME), which contributes to a better understanding of what features in CT/X-ray images characterize the onset of COVID-19.

scholarly journals Multiobjective Genetic Algorithm and Convolutional Neural Network Based COVID-19 Identification in Chest X-Ray Images

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Prashant Kumar Shukla ◽  
Jasminder Kaur Sandhu ◽  
Anamika Ahirwar ◽  
Deepika Ghai ◽  
Priti Maheshwary ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Diagnostic Testing ◽  
The Novel ◽  
Screening Process ◽  
X Ray ◽  
Multiobjective Genetic Algorithm ◽  
Global Pandemic ◽  
Chest X Ray ◽  
Novel Coronavirus ◽  
Fold Cross Validation

COVID-19 is a new disease, caused by the novel coronavirus SARS-CoV-2, that was firstly delineated in humans in 2019.Coronaviruses cause a range of illness in patients varying from common cold to advanced respiratory syndromes such as Severe Acute Respiratory Syndrome (SARS-CoV) and Middle East Respiratory Syndrome (MERS-CoV). The SARS-CoV-2 outbreak has resulted in a global pandemic, and its transmission is increasing at a rapid rate. Diagnostic testing and approaches provide a valuable tool for doctors and support them with the screening process. Automatic COVID-19 identification in chest X-ray images can be useful to test for COVID-19 infection at a good speed. Therefore, in this paper, a framework is designed by using Convolutional Neural Networks (CNN) to diagnose COVID-19 patients using chest X-ray images. A pretrained GoogLeNet is utilized for implementing the transfer learning (i.e., by replacing some sets of final network CNN layers). 20-fold cross-validation is considered to overcome the overfitting quandary. Finally, the multiobjective genetic algorithm is considered to tune the hyperparameters of the proposed COVID-19 identification in chest X-ray images. Extensive experiments show that the proposed COVID-19 identification model obtains remarkably better results and may be utilized for real-time testing of patients.

scholarly journals The use of lung ultrasound during the COVID-19 pandemic

2020 ◽  
Vol 27 (SP1) ◽  
pp. e64-e75
Author(s):  
Aly Youssef ◽  
Marta Cavalera ◽  
Carlotta Azzarone ◽  
Carla Serra ◽  
Elena Brunelli ◽  
...  
Keyword(s):  
Health System ◽  
Pulmonary Infection ◽  
Lung Ultrasound ◽  
The Novel ◽  
Chain Reaction ◽  
X Ray ◽  
Rapid Learning ◽  
Chest X Ray ◽  
Polymerase Chain ◽  
Novel Coronavirus

The novel coronavirus disease (COVID-19) is a challenge to every health system. Unfortunately, it is unlikely that this pandemic will disappear soon. No health system, with its present resources and workflow, is capable enough to deal with a full-blown wave of this pandemic. Acquisition of specific new skills may be fundamental in delivering appropriate health care for our patients. The gold standard for diagnosis of the COVID-19 infection is real-time reverse transcription polymerase chain reaction. Radiological investigations (chest X-ray or high-resolution computerized tomography [CT]) can be helpful both for diagnosis and management, but they have many limitations. Ultrasound has been suggested as a reliable and accurate tool for assessing the lungs in COVID-19 patients. Lung ultrasound (LUS) can show specific signs of inter-stitial pneumonia, which is characteristic of COVID-19 pulmonary infection. In addition, nonradiologist specialists with experience in ultrasound can be trained on LUS with a relatively rapid learning curve. In pregnancy, LUS can be particularly useful due to the avoidance of exposure to ionizing radiation. In this review, we present the advantages, techniques, and limitations of the use of LUS during the COVID-19 pandemic, with specific focus on pregnancy.

scholarly journals A deep learning framework to detect Covid-19 disease via chest X-ray and CT scan images

2021 ◽  
Vol 11 (1) ◽  
pp. 844
Author(s):  
Mohammed Y. Kamil
Keyword(s):  
Deep Learning ◽  
High Performance ◽  
Early Stage ◽  
Diagnostic Technique ◽  
Disease Diagnosis ◽  
X Ray ◽  
Learning Framework ◽  
Chest X Ray ◽  
Low Sensitivity ◽  
Deep Learning Model

COVID-19 disease has rapidly spread all over the world at the beginning of this year. The hospitals' reports have told that low sensitivity of RT-PCR tests in the infection early stage. At which point, a rapid and accurate diagnostic technique, is needed to detect the Covid-19. CT has been demonstrated to be a successful tool in the diagnosis of disease. A deep learning framework can be developed to aid in evaluating CT exams to provide diagnosis, thus saving time for disease control. In this work, a deep learning model was modified to Covid-19 detection via features extraction from chest X-ray and CT images. Initially, many transfer-learning models have applied and comparison it, then a VGG-19 model was tuned to get the best results that can be adopted in the disease diagnosis. Diagnostic performance was assessed for all models used via the dataset that included 1000 images. The VGG-19 model achieved the highest accuracy of 99%, sensitivity of 97.4%, and specificity of 99.4%. The deep learning and image processing demonstrated high performance in early Covid-19 detection. It shows to be an auxiliary detection way for clinical doctors and thus contribute to the control of the pandemic.

scholarly journals Machine learning screening of COVID-19 patients based on X-ray images for unbalanced classes

2021 ◽  
Vol 2021 (2) ◽  
Author(s):  
Ilyes Mrad ◽  
Ridha Hamila ◽  
Nasser Al-Emadi ◽  
Aiman Erbad ◽  
Tahir Hamid ◽  
...  
Keyword(s):  
High Performance ◽  
Emergency Services ◽  
Detection Efficiency ◽  
Diagnostic Technique ◽  
Screening Methods ◽  
Deep Convolutional Neural Networks ◽  
X Ray ◽  
Efficient Detection ◽  
Chest X Ray ◽  
Binary Classifiers

Background: COVID-19 is a pandemic that had already infected more than forty-six million people and caused more than a million deaths by 1st of November 2020. The virus pandemic appears to have had a catastrophic effect on the global population's safety. Therefore, efficient detection of infected patients is a key phase in the battle against COVID-19. One of the main screening methods is radiological testing. The goal of this study is using chest X-ray images to detect COVID-19 pneumonia patients while optimizing detection efficiency. Methods: As shown in, we combined three methods to detect COVID-19 namely: convolutional neural network, transfer learning, and the focal loss function which are used for unbalanced classes, to build three binary classifiers which are COVID-19 versus normal, COVID-19 versus pneumonia, and COVID-19 versus normal pneumonia (normal and pneumonia). The database used includes a mixture of 400 COVID-19, 1,340 viral pneumonia, 2,560 bacterial pneumonia, and 1,340 normal chest X-ray images for training, validation, and testing of four pre-trained deep convolutional neural networks. Then, the pre-trained model that gives the best results was chosen to improve its performances by two enhancement techniques which are image augmentation, allowing us to reach approximately 2,500 images per class, and the adjustment of focal loss hyperparameters. Results: A comparative study was conducted of our proposed classifiers with well-known classifiers and obtained much better results in terms of accuracy, specificity, sensitivity and precision, as illustrated in . Conclusion: The high performance of this computer-aided diagnostic technique may greatly increase the screening speed and reliability of COVID-19 diagnostic cases. Particularly, at the crowded emergency services, it will be particularly helpful in this pandemic when the risk of infection and the necessity for prevention initiatives run contrary to the available resources.

scholarly journals The Rise of Lung Ultrasound in the Era of COVID-19

2021 ◽  
Vol 33 (4) ◽  
pp. 5-8
Author(s):  
Shailendra Kumar Motwani ◽  
Helen Saunders
Keyword(s):  
Lung Ultrasound ◽  
Image Interpretation ◽  
Ultrasound Images ◽  
The Novel ◽  
Non Invasive ◽  
Global Pandemic ◽  
Pleural Surface ◽  
Chest X Ray ◽  
Novel Coronavirus ◽  
Huge Challenge

The current global pandemic caused by the novel coronavirus severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) presents a huge challenge for physicians. Rapid diagnosis, triage and clinical management of these patients is a challenge for physicians but may be aided using lung ultrasound. Lung ultrasound has been in use for over 10 years mainly by critical care practitioners and emergency physicians with variable uptake, but it has gained popularity during the Coronavirus disease-2019 (COVID-19) pandemic as a diagnostic tool and can be easily learned compared to the other ultrasound techniques. Image interpretation is based on identifying artefacts generated by the pleural surface. This technique is non-invasive and can be performed rapidly at the patient’s bedside. It has higher accuracy in diagnosis than auscultation and Chest X-ray (CXR) combined. In this article the authors describe the interpretation of lung ultrasound images, particularly in patients with COVID-19 and discuss indications for this technique. Physicians are recommended to gain familiarity with this technique and use of online resources for guidance.

scholarly journals COVID-19/Pneumonia Classification Based on Guided Attention

2021 ◽  
Author(s):  
Viacheslav Danilov ◽  
Alex Karpovsky ◽  
Alexander Kirpich ◽  
Diana Litmanovich ◽  
Dato Nefaridze ◽  
...  
Keyword(s):  
The Novel ◽  
New Techniques ◽  
Robust Solution ◽  
X Ray ◽  
Machine Learning Methods ◽  
Traditional Classification ◽  
Chest X Ray ◽  
Novel Coronavirus ◽  
Different Sources ◽  
Modern Machine

Abstract With the novel coronavirus 19 (COVID-19) continually having a devastating effect around the globe, many scientists and clinicians are actively seeking to develop new techniques to assist with the tackling of this disease. Modern machine learning methods have shown promise in their adoption to assist the health care industry through their data and analytics-driven decision making, inspiring researchers to develop new angles to fight the virus. In this paper, we aim to develop a robust method for the detection of COVID-19 by utilizing patients' chest X-ray images. Despite recent progress, scarcity of data has thus far limited the development of a robust solution. We extend upon existing work by combing publicly available data across 5 different sources and carefully annotating the comprising images into three categories: normal, pneumonia, and COVID-19. To achieve a high classification accuracy, we propose a training pipeline based on the directed guidance of traditional classification networks, where the guidance is directed by an external segmentation network. Through this network, we observed that the widely used, standard networks can achieve an accuracy comparable to tailor-made models specifically for COVID-19, furthermore one network, VGG-16, outperformed the best of the tailor-made models.

scholarly journals Detection of COVID-19 from Chest X-ray and CT Scan Images using Improved Stacked Sparse Autoencoder

2021 ◽  
Vol 29 (3) ◽  
Author(s):  
Syahril Ramadhan Saufi ◽  
Muhd Danial Abu Hasan ◽  
Zair Asrar Ahmad ◽  
Mohd Salman Leong ◽  
Lim Meng Hee
Keyword(s):  
Ct Scan ◽  
The Novel ◽  
Essential Information ◽  
X Ray ◽  
Chest X Ray ◽  
Sparse Autoencoder ◽  
Stacked Sparse Autoencoder ◽  
Novel Coronavirus ◽  
Deep Learning Model ◽  
Chest Ct Scan

The novel Coronavirus 2019 (COVID-19) has spread rapidly and has become a pandemic around the world. So far, about 44 million cases have been registered, causing more than one million deaths worldwide. COVID-19 has had a devastating impact on every nation, particularly the economic sector. To identify the infected human being and prevent the virus from spreading further, easy, and precise screening is required. COVID-19 can be potentially detected by using Chest X-ray and computed tomography (CT) images, as these images contain essential information of lung infection. This radiology image is usually examined by the expert to detect the presence of COVID-19 symptom. In this study, the improved stacked sparse autoencoder is used to examine the radiology images. According to the result, the proposed deep learning model was able to achieve a classification accuracy of 96.6% and 83.0% for chest X-ray and chest CT-scan images, respectively.

scholarly journals Detection of COVID-19 from Chest X-ray Images Using Deep Convolutional Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5940
Author(s):  
Natheer Khasawneh ◽  
Mohammad Fraiwan ◽  
Luay Fraiwan ◽  
Basheer Khassawneh ◽  
Ali Ibnian
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Detection Accuracy ◽  
Deep Convolutional Neural Networks ◽  
Local Data ◽  
X Ray ◽  
The Public ◽  
Global Pandemic ◽  
Chest X Ray ◽  
Public Dataset

The COVID-19 global pandemic has wreaked havoc on every aspect of our lives. More specifically, healthcare systems were greatly stretched to their limits and beyond. Advances in artificial intelligence have enabled the implementation of sophisticated applications that can meet clinical accuracy requirements. In this study, customized and pre-trained deep learning models based on convolutional neural networks were used to detect pneumonia caused by COVID-19 respiratory complications. Chest X-ray images from 368 confirmed COVID-19 patients were collected locally. In addition, data from three publicly available datasets were used. The performance was evaluated in four ways. First, the public dataset was used for training and testing. Second, data from the local and public sources were combined and used to train and test the models. Third, the public dataset was used to train the model and the local data were used for testing only. This approach adds greater credibility to the detection models and tests their ability to generalize to new data without overfitting the model to specific samples. Fourth, the combined data were used for training and the local dataset was used for testing. The results show a high detection accuracy of 98.7% with the combined dataset, and most models handled new data with an insignificant drop in accuracy.

scholarly journals Deep Learning Model for Improving the Characterization of Coronavirus on Chest X-ray Images Using CNN

2020 ◽  
Author(s):  
Olaide N. Oyelade ◽  
Absalom E. Ezugwu
Keyword(s):  
Deep Learning ◽  
Language Processing ◽  
Data Augmentation ◽  
Knowledge Engineering ◽  
The Novel ◽  
X Ray ◽  
Proposed Model ◽  
Chest X Ray ◽  
Novel Coronavirus

AbstractThe novel Coronavirus, also known as Covid19, is a pandemic that has weighed heavily on the socio-economic affairs of the world. Although researches into the production of relevant vaccine are being advanced, there is, however, a need for a computational solution to mediate the process of aiding quick detection of the disease. Different computational solutions comprised of natural language processing, knowledge engineering and deep learning have been adopted for this task. However, deep learning solutions have shown interesting performance compared to other methods. This paper therefore aims to advance the application deep learning technique to the problem of characterization and detection of novel coronavirus. The approach adopted in this study proposes a convolutional neural network (CNN) model which is further enhanced using the technique of data augmentation. The motive for the enhancement of the CNN model through the latter technique is to investigate the possibility of further improving the performances of deep learning models in detection of coronavirus. The proposed model is then applied to the COVID-19 X-ray dataset in this study which is the National Institutes of Health (NIH) Chest X-Ray dataset obtained from Kaggle for the purpose of promoting early detection and screening of coronavirus disease. Results obtained showed that our approach achieved a performance of 100% accuracy, recall/precision of 0.85, F-measure of 0.9, and specificity of 1.0. The proposed CNN model and data augmentation solution may be adopted in pre-screening suspected cases of Covid19 to provide support to the use of the well-known RT-PCR testing.

scholarly journals CCBlock based on deep learning for diagnosis COVID-19 in chest x-ray image

2020 ◽  
Author(s):  
Ali Al-Bawi ◽  
Karrar Ali Al-Kaabi ◽  
Mohammed Jeryo ◽  
Ahmad Al-Fatlawi
Keyword(s):  
Well Being ◽  
Healthy People ◽  
Pcr Analysis ◽  
The Novel ◽  
X Ray ◽  
Detection Systems ◽  
Testing Dataset ◽  
Rapid Spread ◽  
Chest X Ray ◽  
Novel Coronavirus

Abstract Propose: Troubling countries one after another, the COVID-19 pandemic has dramatically affected the health and well-being of the world's population. The disease may continue to persist more extensively due to the increasing number of new cases daily, the rapid spread of the virus, and delay in the PCR analysis results. Therefore, it is necessary to consider developing assistive methods for detecting and diagnosing the COVID-19 to eradicate the spread of the novel coronavirus among people. Based on convolutional neural networks (CNNs), automated detection systems have shown promising results of diagnosing patients with the COVID-19 through radiography; thus, they are introduced as a workable solution to the COVID-19 diagnosis.Materials and Methods: Based on the enhancement of the classical visual geometry group (VGG) network with the convolutional COVID block (CCBlock), an efficient screening model was proposed in this study to diagnose and distinguish patients with the COVID-19 from those with pneumonia and the healthy people through radiography. The model testing dataset included 1,828 x-ray images available on public platforms. 310 images were showing confirmed COVID-19 cases, 864 images indicating pneumonia cases, and 654 images showing healthy people.Results: According to the test results, enhancing the classical VGG network with radiography provided the highest diagnosis performance and overall accuracy of 98.52% for two classes as well as accuracy of 95.34% for three classes.Conclusions: According to the results, using the enhanced VGG deep neural network can help radiologists automatically diagnose the COVID-19 through radiography.

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