scholarly journals Analysis of COVID-19 Chest X-ray Data using an Ensemble of Different Convolutional Architectures

2021 ◽  
Vol 9 (10) ◽  
pp. 1830-1835
Author(s):  
Gaurav Sharma
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Oxygen Saturation ◽  
Common Cold ◽  
Body Part ◽  
X Ray ◽  
Proposed Model ◽  
Unique Model ◽  
Viral Influenza ◽  
Chest X Ray

Abstract: After every 100 years, a pandemic comes and takes a great toll on the global civilization. This time its COVID-19 and the aftereffects are terrifying. As the symptoms for the disease are very common and are similar to common cold and viral influenza, the detection from symptoms is quite difficult. Although there are many methods devised but the detection of COVID19 has been a problem since the start, and we are still struggling to identify whether a person has the disease. This study proposes a unique model to identify the positive and negative cases using X-ray images of an individual as lungs are the first and most critical body part which gets affected by the virus which causes a deprecation in oxygen saturation. The proposed model is an ensemble of different CNN architectures which are Dense Net, NasNet-Large, Resnet-50, Inception Net, EfficientNetB0 and EfficientNetB1. The results show that the model reaches an accuracy of 99.6% on the tested dataset. Keywords: Deep learning, Convolutional Neural Networks, COVID-19, Ensemble Learning, EfficientNet

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

2020 ◽  
Vol 25 (6) ◽  
pp. 553-565 ◽  
Author(s):  
Boran Sekeroglu ◽  
Ilker Ozsahin
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Statistical Data ◽  
Area Under The Curve ◽  
Operating Characteristics ◽  
X Ray ◽  
Life Saving ◽  
Chest X Ray

The detection of severe acute respiratory syndrome coronavirus 2 (SARS CoV-2), which is responsible for coronavirus disease 2019 (COVID-19), using chest X-ray images has life-saving importance for both patients and doctors. In addition, in countries that are unable to purchase laboratory kits for testing, this becomes even more vital. In this study, we aimed to present the use of deep learning for the high-accuracy detection of COVID-19 using chest X-ray images. Publicly available X-ray images (1583 healthy, 4292 pneumonia, and 225 confirmed COVID-19) were used in the experiments, which involved the training of deep learning and machine learning classifiers. Thirty-eight experiments were performed using convolutional neural networks, 10 experiments were performed using five machine learning models, and 14 experiments were performed using the state-of-the-art pre-trained networks for transfer learning. Images and statistical data were considered separately in the experiments to evaluate the performances of models, and eightfold cross-validation was used. A mean sensitivity of 93.84%, mean specificity of 99.18%, mean accuracy of 98.50%, and mean receiver operating characteristics–area under the curve scores of 96.51% are achieved. A convolutional neural network without pre-processing and with minimized layers is capable of detecting COVID-19 in a limited number of, and in imbalanced, chest X-ray images.

scholarly journals A New Deep Learning Pipeline to Detect Covid-19 on Chest X-Ray Images using Local Binary Pattern, Dual Tree Complex Wavelet Transform and Convolutional Neural Networks

2020 ◽  
Author(s):  
Huseyin Yaşar ◽  
Murat Ceylan
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Wavelet Transform ◽  
Test Data ◽  
Local Binary Pattern ◽  
Complex Wavelet Transform ◽  
Data Set ◽  
X Ray ◽  
Chest X Ray ◽  
Sensitivity Specificity

Abstract At the end of 2019, a new type of virus, belonging to the coronaviridae family has emerged and it is considered that the virus in question is of zootonic origin. The virus that emerged in China first affected this country and then spread worldwide. Pneumonia develops due to Covid-19 virus in patients having severe disease symptoms. Many literature studies have been carried out in the process where the effects of the disease-induced pneumonia in lungs have been demonstrated with the help of chest X-ray imaging. In this study, which aims at early diagnosis of Covid-19 disease by using X-Ray images, the deep-learning approach, which is a state-of-the-art artificial intelligence method, was used and automatic classification of images was performed using Convolutional Neural Networks (CNN). In the first training-test data set used in the study, there were a total of 230 abnormal and 80 normal X-Ray images, while in the second training-test data set there were 476 X-Ray images, of which 150 abnormal and 326 normal. Thus, classification results have been provided for two data sets, containing predominantly abnormal images and predominantly normal images respectively. In the study, a 23-layer CNN architecture was developed. Within the scope of the study, results were obtained by using chest X-Ray images directly in training-test procedures and the sub-band images obtained by applying Dual Tree Complex Wavelet Transform (DT-CWT) to the above-mentioned images. The same experiments were repeated using images obtained by applying Local Binary Pattern (LBP) to the chest X-Ray images. Within the scope of the study, a new result generation algorithm having been put forward additionally, it was ensured that the experimental results were combined and the success of the study was improved. In the experiments carried out in the study, the trainings were carried out using the k-fold cross validation method. Here the k value was chosen 23. Considering the highest results of the tests performed in the study, values of sensitivity, specificity, accuracy and AUC for the first training-test data set were calculated to be 1, 1, 0,9913 and 0,9996; while for the second data set of training-test, they were 1, 0,9969, 0,9958 and 0,9996 respectively. Considering the average highest results of the experiments performed within the scope of the study, the values of sensitivity, specificity, accuracy and AUC for the first training-test data set were 0,9933, 0,9725, 0,9843 and 0,9988; while for the second training-test data set, they were 0,9813, 0,9908, 0,9857 and 0,9983 respectively.

scholarly journals An Effective Convolutional Neural Network Model for the Early Detection of COVID-19 Using Chest X-ray Images

2021 ◽  
Vol 11 (21) ◽  
pp. 10301
Author(s):  
Muhammad Shoaib Farooq ◽  
Attique Ur Rehman ◽  
Muhammad Idrees ◽  
Muhammad Ahsan Raza ◽  
Jehad Ali ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Real World ◽  
Early Stage ◽  
Binary Classification ◽  
X Rays ◽  
X Ray ◽  
Proposed Model ◽  
Chest X Ray

COVID-19 has been difficult to diagnose and treat at an early stage all over the world. The numbers of patients showing symptoms for COVID-19 have caused medical facilities at hospitals to become unavailable or overcrowded, which is a major challenge. Studies have recently allowed us to determine that COVID-19 can be diagnosed with the aid of chest X-ray images. To combat the COVID-19 outbreak, developing a deep learning (DL) based model for automated COVID-19 diagnosis on chest X-ray is beneficial. In this research, we have proposed a customized convolutional neural network (CNN) model to detect COVID-19 from chest X-ray images. The model is based on nine layers which uses a binary classification method to differentiate between COVID-19 and normal chest X-rays. It provides COVID-19 detection early so the patients can be admitted in a timely fashion. The proposed model was trained and tested on two publicly available datasets. Cross-dataset studies are used to assess the robustness in a real-world context. Six hundred X-ray images were used for training and two hundred X-rays were used for validation of the model. The X-ray images of the dataset were preprocessed to improve the results and visualized for better analysis. The developed algorithm reached 98% precision, recall and f1-score. The cross-dataset studies also demonstrate the resilience of deep learning algorithms in a real-world context with 98.5 percent accuracy. Furthermore, a comparison table was created which shows that our proposed model outperforms other relative models in terms of accuracy. The quick and high-performance of our proposed DL-based customized model identifies COVID-19 patients quickly, which is helpful in controlling the COVID-19 outbreak.

scholarly journals Using a Deep Learning Model to Explore the Impact of Clinical Data on COVID-19 Diagnosis Using Chest X-ray

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 669
Author(s):  
Irfan Ullah Khan ◽  
Nida Aslam ◽  
Talha Anwar ◽  
Hind S. Alsaif ◽  
Sara Mhd. Bachar Chrouf ◽  
...  
Keyword(s):  
Deep Learning ◽  
Clinical Data ◽  
Disease Diagnosis ◽  
University Hospital ◽  
X Ray ◽  
Clinical Patient ◽  
Proposed Model ◽  
Chest X Ray ◽  
The Impact ◽  
Deep Learning Model

The coronavirus pandemic (COVID-19) is disrupting the entire world; its rapid global spread threatens to affect millions of people. Accurate and timely diagnosis of COVID-19 is essential to control the spread and alleviate risk. Due to the promising results achieved by integrating machine learning (ML), particularly deep learning (DL), in automating the multiple disease diagnosis process. In the current study, a model based on deep learning was proposed for the automated diagnosis of COVID-19 using chest X-ray images (CXR) and clinical data of the patient. The aim of this study is to investigate the effects of integrating clinical patient data with the CXR for automated COVID-19 diagnosis. The proposed model used data collected from King Fahad University Hospital, Dammam, KSA, which consists of 270 patient records. The experiments were carried out first with clinical data, second with the CXR, and finally with clinical data and CXR. The fusion technique was used to combine the clinical features and features extracted from images. The study found that integrating clinical data with the CXR improves diagnostic accuracy. Using the clinical data and the CXR, the model achieved an accuracy of 0.970, a recall of 0.986, a precision of 0.978, and an F-score of 0.982. Further validation was performed by comparing the performance of the proposed system with the diagnosis of an expert. Additionally, the results have shown that the proposed system can be used as a tool that can help the doctors in COVID-19 diagnosis.

scholarly journals Metaheuristic-based Deep COVID-19 Screening Model from Chest X-Ray Images

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Manjit Kaur ◽  
Vijay Kumar ◽  
Vaishali Yadav ◽  
Dilbag Singh ◽  
Naresh Kumar ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Models ◽  
Huge Number ◽  
X Ray ◽  
Screening Model ◽  
Proposed Model ◽  
Strength Pareto Evolutionary Algorithm ◽  
Chest X Ray ◽  
Deep Learning Model

COVID-19 has affected the whole world drastically. A huge number of people have lost their lives due to this pandemic. Early detection of COVID-19 infection is helpful for treatment and quarantine. Therefore, many researchers have designed a deep learning model for the early diagnosis of COVID-19-infected patients. However, deep learning models suffer from overfitting and hyperparameter-tuning issues. To overcome these issues, in this paper, a metaheuristic-based deep COVID-19 screening model is proposed for X-ray images. The modified AlexNet architecture is used for feature extraction and classification of the input images. Strength Pareto evolutionary algorithm-II (SPEA-II) is used to tune the hyperparameters of modified AlexNet. The proposed model is tested on a four-class (i.e., COVID-19, tuberculosis, pneumonia, or healthy) dataset. Finally, the comparisons are drawn among the existing and the proposed models.

scholarly journals A Deep Learning Approach to Detect COVID-19 Patients from Chest X-ray Images

AI ◽  
2020 ◽  
Vol 1 (3) ◽  
pp. 418-435
Author(s):  
Khandaker Haque ◽  
Ahmed Abdelgawad
Keyword(s):  
Deep Learning ◽  
World Health ◽  
X Rays ◽  
X Ray ◽  
Global Pandemic ◽  
Proposed Model ◽  
Dental Diseases ◽  
Chest X Ray ◽  
Artery Disease ◽  
Health Organization

Deep Learning has improved multi-fold in recent years and it has been playing a great role in image classification which also includes medical imaging. Convolutional Neural Networks (CNNs) have been performing well in detecting many diseases including coronary artery disease, malaria, Alzheimer’s disease, different dental diseases, and Parkinson’s disease. Like other cases, CNN has a substantial prospect in detecting COVID-19 patients with medical images like chest X-rays and CTs. Coronavirus or COVID-19 has been declared a global pandemic by the World Health Organization (WHO). As of 8 August 2020, the total COVID-19 confirmed cases are 19.18 M and deaths are 0.716 M worldwide. Detecting Coronavirus positive patients is very important in preventing the spread of this virus. On this conquest, a CNN model is proposed to detect COVID-19 patients from chest X-ray images. Two more CNN models with different number of convolution layers and three other models based on pretrained ResNet50, VGG-16 and VGG-19 are evaluated with comparative analytical analysis. All six models are trained and validated with Dataset 1 and Dataset 2. Dataset 1 has 201 normal and 201 COVID-19 chest X-rays whereas Dataset 2 is comparatively larger with 659 normal and 295 COVID-19 chest X-ray images. The proposed model performs with an accuracy of 98.3% and a precision of 96.72% with Dataset 2. This model gives the Receiver Operating Characteristic (ROC) curve area of 0.983 and F1-score of 98.3 with Dataset 2. Moreover, this work shows a comparative analysis of how change in convolutional layers and increase in dataset affect classifying performances.

COVID-19 Chest X-Ray Images Diagnosis: A Neutrosophic and Deep Transfer Learning Approach

2020 ◽  
Author(s):  
Nour Eldeen M. Khalifa ◽  
Florentin Smarandache ◽  
Mohamed Loey
Keyword(s):  
Deep Learning ◽  
Performance Metrics ◽  
World Health ◽  
Transfer Model ◽  
X Ray ◽  
Proposed Model ◽  
The World ◽  
Chest X Ray ◽  
Testing Accuracy ◽  
Transfer Models

Coronavirus, also known as COVID-19, has spread to several countries around the world. It was announced as a pandemic disease by The World Health Organization (WHO) in 2020 for its devastating impact on humans. With the advancements in computer science algorithms, the detection of this type of virus in the early stages is urgently needed for the fast recovery of patients. In this paper, a neutrosophic with a deep learning model for the detection of COVID-19 from chest X-ray medical digital images is presented. The proposed model relies on neutrosophic theory by converting the medical images from the grayscale spatial domain to the neutrosophic domain. The neutrosophic domain consists of three types of images and they are, the True (T) images, the Indeterminacy (I) images, and the Falsity (F) images. Using neutrosophic images has positively affected the accuracy of the proposed model. The dataset used in this research has been collected from different sources as there is no benchmark dataset for COVID-19 chest X-ray until the writing of this research. The dataset consists of four classes and they are COVID-19, Normal, Pneumonia bacterial, and Pneumonia virus. After the conversion to the neutrosophic domain, the images are fed into three different deep transfer models and they are Alexnet, Googlenet, and Restnet18. Those models are selected as they have a small number of layers on their architectures and they have been used with related work. To test the performance of the conversion to the neutrosophic domain, four scenarios have been tested. The first scenario is training the deep transfer models with True (T) neutrosophic images only. The second one is training on Indeterminacy (I) neutrosophic images, while the third scenario is training the deep models over the Falsity (F) neutrosophic images. The fourth scenario is training over the combined (T, I, F) neutrosophic images. According to the experimental results, the combined (T, I, F) neutrosophic images achieved the highest accuracy possible for the validation, testing and all performance metrics such Precision, Recall and F1 Score using Resnet18 as a deep transfer model. The proposed model achieved a testing accuracy with 78.70%. Furthermore, the proposed model using neutrosophic and Resnet18 had achieved superior testing accuracy with a related work which achieved 52.80% with the same experimental environmental setup and the same deep learning hyperparameters.

scholarly journals CoviDecode : Detection of COVID-19 from Chest X-Ray images using Convolutional Neural Networks

2020 ◽  
Vol 6 (12) ◽  
pp. 436-439
Author(s):  
Rishabh Raj
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Statistical Data ◽  
Virus Disease ◽  
X Ray ◽  
Corona Virus ◽  
Life Saving ◽  
Chest X Ray

ommand, product recommendation and medical diagnosis. The detection of severe acute respiratory syndrome corona virus 2 (SARS CoV-2), which is responsible for corona virus disease 2019 (COVID-19), using chest X-ray images has life-saving importance for bothpatients and doctors. In addition, in countries that are unable to purchase laboratory kits for testing, this becomes even more vital. In this study, we aimed to present the use of deep learning for the high-accuracy detection of COVID-19 using chest X-ray images. Publicly available X-ray images were used in the experiments, which involved the training of deep learning and machine learning classifiers. Experiments were performed using convolutional neural networks and machine learning models. Images and statistical data were considered separately in the experiments to evaluate the performances of models, and eightfold cross-validation was used. A mean accuracy of 98.50%. A convolutional neural network without pre-processing and with minimized layers is capable of detecting COVID- 19 in a limited number of, and in imbalanced, chest X-rayimages.

scholarly journals The Practicality of Deep Learning Algorithms in COVID-19 Detection: Application to Chest X-ray Images

Algorithms ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 183
Author(s):  
Abdulaziz Alorf
Keyword(s):  
Computed Tomography ◽  
Neural Networks ◽  
Deep Learning ◽  
Medical Personnel ◽  
Automated Analysis ◽  
Main Method ◽  
X Ray ◽  
The World ◽  
Chest X Ray ◽  
Trained Physician

Since January 2020, the outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has affected the whole world, producing a respiratory disease that can become severe and even cause death in certain groups of people. The main method for diagnosing coronavirus disease 2019 (COVID-19) is performing viral tests. However, the kits for carrying out these tests are scarce in certain regions of the world. Lung conditions as perceived in computed tomography and radiography images exhibit a high correlation with the presence of COVID-19 infections. This work attempted to assess the feasibility of using convolutional neural networks for the analysis of pulmonary radiography images to distinguish COVID-19 infections from non-infected cases and other types of viral or bacterial pulmonary conditions. The results obtained indicate that these networks can successfully distinguish the pulmonary radiographies of COVID-19-infected patients from radiographies that exhibit other or no pathology, with a sensitivity of 100% and specificity of 97.6%. This could help future efforts to automate the process of identifying lung radiography images of suspicious cases, thereby supporting medical personnel when many patients need to be rapidly checked. The automated analysis of pulmonary radiography is not intended to be a substitute for formal viral tests or formal diagnosis by a properly trained physician but rather to assist with identification when the need arises.

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