scholarly journals Deep Learning Fusion for COVID-19 Diagnosis

2020 ◽  
Author(s):  
Odysseas Kechagias-Stamatis ◽  
Nabil Aouf ◽  
John A. Koukos
Keyword(s):  
Deep Learning ◽  
Classification Problem ◽  
The Novel ◽  
X Ray ◽  
Deep Network ◽  
Current State ◽  
Medical Imagery ◽  
Learning Techniques ◽  
Novel Coronavirus ◽  
Fusion Scheme

AbstractThe outbreak of the novel coronavirus (COVID-19) disease has spurred a tremendous research boost aiming at controlling it. Under this scope, deep learning techniques have received even more attention as an asset to automatically detect patients infected by COVID-19 and reduce the doctor’s burden to manually assess medical imagery. Thus, this work considers a deep learning architecture that fuses the layers of current-state-of-the-art deep networks to produce a new structure-fused deep network. The advantages of our deep network fusion scheme are multifold, and ultimately afford an appealing COVID-19 automatic diagnosis that outbalances current deep learning methods. Indeed, evaluation on Computer Tomography (CT) and X-ray imagery considering a two-class (COVID-19/ non-COVID-19) and a four-class (COVID-19/ non-COVID-19/ Pneumonia bacterial / Pneumonia virus) classification problem, highlights the classification capabilities of our method attaining 99.3% and 100%, respectively.

scholarly journals Transfer deep learning approach for detecting coronavirus disease in X-ray images

2021 ◽  
Vol 11 (6) ◽  
pp. 4999
Author(s):  
Mohammed Al-Smadi ◽  
Mahmoud Hammad ◽  
Qanita Bani Baker ◽  
Saja Khaled Tawalbeh ◽  
Sa’ad A. Al-Zboon
Keyword(s):  
Deep Learning ◽  
Clinical Symptoms ◽  
Binary Classification ◽  
Classification Problem ◽  
Learning Approach ◽  
The Novel ◽  
X Ray ◽  
Task Classification ◽  
High Infection ◽  
Novel Coronavirus

<p><span lang="EN-US">Currently, the whole world is fighting a very dangerous and infectious disease caused by the novel coronavirus, called COVID-19. The COVID-19 is rapidly spreading around the world due to its high infection rate. Therefore, early discovery of COVID-19 is crucial to better treat the infected person as well as to slow down the spread of this virus. However, the current solution for detecting COVID-19 cases including the PCR test, CT images, epidemiologically history, and clinical symptoms suffer from high false positive. To overcome this problem, we have developed a novel transfer deep learning approach for detecting COVID-19 based on x-ray images. Our approach helps medical staff in determining if a patient is normal, has COVID-19, or other pneumonia. Our approach relies on pre-trained models including Inception-V3, Xception, and MobileNet to perform two tasks: i) binary classification to determine if a person infected with COVID-19 or not and ii) a multi-task classification problem to distinguish normal, COVID-19, and pneumonia cases. Our experimental results on a large dataset show that the F1-score is 100% in the first task and 97.66 in the second task.</span></p>

scholarly journals A holistic comparison between deep learning techniques to determine Covid-19 patients utilizing chest X-Ray images

2020 ◽  
Vol 3 (4) ◽  
pp. 85-93
Author(s):  
Taki Hasan Rafi ◽  
Keyword(s):  
Deep Learning ◽  
Medical Community ◽  
Test Cases ◽  
Suitable Model ◽  
Testing Time ◽  
Rt Pcr ◽  
X Ray ◽  
Learning Techniques ◽  
Chest X Ray ◽  
Novel Coronavirus

Novel coronavirus likewise called COVID-19 began in Wuhan, China in December 2019 and has now outspread over the world. Around 63 millions of people currently got influenced by novel coronavirus and it causes around 1,500,000 deaths. There are just about 600,000 individuals contaminated by COVID-19 in Bangladesh too. As it is an exceptionally new pandemic infection, its diagnosis is challenging for the medical community. In regular cases, it is hard for lower incoming countries to test cases easily. RT-PCR test is the most generally utilized analysis framework for COVID-19 patient detection. However, by utilizing X-ray image based programmed recognition can diminish the expense and testing time. So according to handling this test, it is important to program and effective recognition to forestall transmission to others. In this paper, author attempts to distinguish COVID-19 patients by chest X-ray images. Author executes various pre-trained deep learning models on the dataset such as Base-CNN, ResNet-50, DenseNet-121 and EfficientNet-B4. All the outcomes are compared to determine a suitable model for COVID-19 detection using chest X-ray images. Author also evaluates the results by AUC, where EfficientNet-B4 has 0.997 AUC, ResNet-50 has 0.967 AUC, DenseNet-121 has 0.874 AUC and the Base-CNN model has 0.762 AUC individually. The EfficientNet-B4 has achieved 98.86% 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.

COVID-19 Detection Using Chest X-Ray and Transfer Learning

2021 ◽  
pp. 171-186
Author(s):  
Aditya Sharma ◽  
Arshdeep Singh Chudey ◽  
Mrityunjay Singh
Keyword(s):  
Performance Evaluation ◽  
Deep Learning ◽  
Confusion Matrix ◽  
Receiver Operating Curve ◽  
The Novel ◽  
Learning Models ◽  
X Ray ◽  
Major Outbreak ◽  
Chest X Ray ◽  
Novel Coronavirus

The novel coronavirus (COVID-19), which started in the Wuhan province of China, prompted a major outbreak that culminated in a worldwide pandemic. Several cases are being recorded across the globe, with deaths being close to 2.5 million. The increased number of cases and the newness of such a pandemic has resulted in the hospitals being under-equipped leading to problems in diagnosis of the disease. From previous studies, radiography has proved to be the fastest testing method. A screening test using the x-ray scan of the chest region has proved to be effective. For this method, a trained radiologist is needed to detect the disease. Automating this process using deep learning models can prove to be effective. Due to the lack of large dataset, pre-trained CNN models are used in this study. Several models have been employed like VGG-16, Resnet-50, InceptionV3, and InceptionResnetV2. Resnet-50 provided the best accuracy of 98.3%. The performance evaluation has been done using metrics like receiver operating curve and confusion matrix.

scholarly journals A Review on Deep Learning Techniques for the Diagnosis of Novel Coronavirus (COVID-19)

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 30551-30572
Author(s):  
Md. Milon Islam ◽  
Fakhri Karray ◽  
Reda Alhajj ◽  
Jia Zeng
Keyword(s):  
Deep Learning ◽  
Learning Techniques ◽  
Novel Coronavirus

scholarly journals Deep Learning for Laryngopharyngeal Reflux Diagnosis

2021 ◽  
Vol 11 (11) ◽  
pp. 4753
Author(s):  
Gen Ye ◽  
Chen Du ◽  
Tong Lin ◽  
Yan Yan ◽  
Jack Jiang
Keyword(s):  
Deep Learning ◽  
Speech Processing ◽  
Data Augmentation ◽  
Laryngopharyngeal Reflux ◽  
Ph Monitoring ◽  
Binary Classification ◽  
Classification Problem ◽  
Learning Approaches ◽  
Learning Techniques ◽  
Auc Value

(1) Background: Deep learning has become ubiquitous due to its impressive performance in various domains, such as varied as computer vision, natural language and speech processing, and game-playing. In this work, we investigated the performance of recent deep learning approaches on the laryngopharyngeal reflux (LPR) diagnosis task. (2) Methods: Our dataset is composed of 114 subjects with 37 pH-positive cases and 77 control cases. In contrast to prior work based on either reflux finding score (RFS) or pH monitoring, we directly take laryngoscope images as inputs to neural networks, as laryngoscopy is the most common and simple diagnostic method. The diagnosis task is formulated as a binary classification problem. We first tested a powerful backbone network that incorporates residual modules, attention mechanism and data augmentation. Furthermore, recent methods in transfer learning and few-shot learning were investigated. (3) Results: On our dataset, the performance is the best test classification accuracy is 73.4%, while the best AUC value is 76.2%. (4) Conclusions: This study demonstrates that deep learning techniques can be applied to classify LPR images automatically. Although the number of pH-positive images used for training is limited, deep network can still be capable of learning discriminant features with the advantage of technique.

COVID-19 Detection: An Approach Using X-Ray Images and Deep Learning Techniques

2021 ◽  
pp. 7-16
Author(s):  
Akshay Raina ◽  
Shubham Mahajan ◽  
Ch. Vanipriya ◽  
Anil Bhardwaj ◽  
Amit Kant Pandit
Keyword(s):  
Deep Learning ◽  
X Ray ◽  
Learning Techniques

scholarly journals Monitoring the Impact of Air Quality on the COVID-19 Fatalities in Delhi, India: Using Machine Learning Techniques

2020 ◽  
pp. 1-8
Author(s):  
Jasleen Kaur Sethi ◽  
Mamta Mittal
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Air Pollutants ◽  
Machine Learning Techniques ◽  
Environmental Restoration ◽  
The Novel ◽  
Ozone Pollution ◽  
Learning Techniques ◽  
Novel Coronavirus ◽  
The Impact

ABSTRACT Objective: The focus of this study is to monitor the effect of lockdown on the various air pollutants due to the coronavirus disease (COVID-19) pandemic and identify the ones that affect COVID-19 fatalities so that measures to control the pollution could be enforced. Methods: Various machine learning techniques: Decision Trees, Linear Regression, and Random Forest have been applied to correlate air pollutants and COVID-19 fatalities in Delhi. Furthermore, a comparison between the concentration of various air pollutants and the air quality index during the lockdown period and last two years, 2018 and 2019, has been presented. Results: From the experimental work, it has been observed that the pollutants ozone and toluene have increased during the lockdown period. It has also been deduced that the pollutants that may impact the mortalities due to COVID-19 are ozone, NH3, NO2, and PM10. Conclusions: The novel coronavirus has led to environmental restoration due to lockdown. However, there is a need to impose measures to control ozone pollution, as there has been a significant increase in its concentration and it also impacts the COVID-19 mortality rate.

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