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

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.

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Automatically identify COVID-19 and Pneumonia patients using Deep learning in Chest X-rays image

World Journal of Advanced Research and Reviews ◽

10.30574/wjarr.2021.12.3.0662 ◽

2021 ◽

Vol 12 (3) ◽

pp. 011-019

Author(s):

Haris Uddin Sharif ◽

Shaamim Udding Ahmed

Keyword(s):

Neural Network ◽

Deep Learning ◽

Infected Patient ◽

World Health ◽

X Rays ◽

Popular Approach ◽

X Ray ◽

The World ◽

Chest X Ray ◽

Health Organization

At the end of 2019, a new kind of coronavirus (SARS-CoV-2) suffered worldwide and has become the pandemic coronavirus (COVID-19). The outbreak of this virus let to crisis around the world and kills millions of people globally. On March 2020, WHO (World Health Organization) declared it as pandemic disease. The first symptom of this virus is identical to flue and it destroys the human respiratory system. For the identification of this disease, the first key step is the screening of infected patients. The easiest and most popular approach for screening of the COVID-19 patients is chest X-ray images. In this study, our aim to automatically identify the COVID-19 and Pneumonia patients by the X-ray image of infected patient. To identify COVID19 and Pneumonia disease, the convolution Neural Network was training on publicly available dataset on GitHub and Kaggle. The model showed the 98% and 96% training accuracy for three and four classes respectively. The accuracy scores showed the robustness of both model and efficiently deployment for identification of COVID-19 patients.

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Deep Learning Detection of COVID-19, Temporal Variation and its Link with Temperature in Nigeria

Asian Journal of Research in Computer Science ◽

10.9734/ajrcos/2021/v7i330182 ◽

2021 ◽

pp. 55-62

Author(s):

Ebiendele Eromosele Precious

Keyword(s):

Deep Learning ◽

Health Workers ◽

World Health ◽

Religious Activities ◽

X Ray ◽

Global Pandemic ◽

First Case ◽

Average Accuracy ◽

Maximum Temperatures ◽

Health Organization

COVID-19 was announced as a global pandemic on 11 March 2020 by the World Health Organization due to its spread globally. Nigeria recorded its first case on 27 February 2020. Since then, it has spread to all parts of the country. In this paper we study the effectiveness and skill performance of deep learning architectures in assisting health workers in detecting COVID-19 infected patient through X-ray images. Analytical deductions obtained from 500 X-ray images of both infected and non-infected patients confirmed that our proposed model InceptionV3 is effective in detecting COVID-19 and attain an average accuracy of 92%. The relationship or link between the COVID-19 daily occurrence and two meteorological variables (minimum and maximum temperatures) are further assessed. The result also indicated that the cases recorded in Wednesdays and Fridays are observed to be higher than other days which usually coincide with either religious activities or market days in the country, while a progressively decline in weekday cases is observed towards the weekend with Sundays (ranging from 152 to 280 cases) having the lowest cases. The study further indicated statistically that COVID-19 daily cases significantly decline when maximum and minimum temperature are increasing (-0.79 and -0.44 correlation coefficient).

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

Applied Sciences ◽

10.3390/app112110301 ◽

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.

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COVIDPEN: A Novel COVID-19 Detection Model using Chest X-Rays and CT Scans

10.1101/2020.07.08.20149161 ◽

2020 ◽

Author(s):

Amit Kumar Jaiswal ◽

Prayag Tiwari ◽

Vipin Kumar Rathi ◽

Jia Qian ◽

Hari Mohan Pandey ◽

...

Keyword(s):

False Negative ◽

Healthcare Providers ◽

False Negative Rate ◽

X Rays ◽

X Ray ◽

Detection Model ◽

Global Pandemic ◽

High False Negative Rate ◽

Proposed Model ◽

Chest X Ray

The trending global pandemic of COVID-19 is the fastest ever impact which caused people worldwide by severe acute respiratory syndrome~(SARS)-driven coronavirus. However, several countries suffer from the shortage of test kits and high false negative rate in PCR test. Enhancing the chest X-ray or CT detection rate becomes critical. The patient triage is of utmost importance and the use of machine learning can drive the diagnosis of chest X-ray or CT image by identifying COVID-19 cases. To tackle this problem, we propose~COVIDPEN~-~a transfer learning approach on Pruned EfficientNet-based model for the detection of COVID-19 cases. The proposed model is further interpolated by post-hoc analysis for the explainability of the predictions. The effectiveness of our proposed model is demonstrated on two systematic datasets of chest radiographs and computed tomography scans. Experimental results with several baseline comparisons show that our method is on par and confers clinically explicable instances, which are meant for healthcare providers.

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

10.20944/preprints202004.0371.v1 ◽

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.

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Modified AlexNet Convolution Neural Network For Covid-19 Detection Using Chest X-ray Images

Kurdistan Journal of Applied Research ◽

10.24017/covid.14 ◽

2020 ◽

pp. 119-130

Author(s):

Shadman Q. Salih ◽

Hawre Kh. Abdulla ◽

Zanear Sh. Ahmed ◽

Nigar M. Shafiq Surameery ◽

Rasper Dh. Rashid

Keyword(s):

Neural Network ◽

Deep Learning ◽

Imaging Techniques ◽

Convolution Neural Network ◽

World Health ◽

X Ray ◽

Chest X Ray ◽

Health Organization ◽

Fully Connected ◽

The City

First outbreak of COVID-19 was in the city of Wuhan in China in Dec.2019 and then it becomes a pandemic disease all around the world. World Health Organization (WHO) confirmed more than 5.5 million cases and 341,155 deaths from the disease till the time of writing this paper. This new worldwide disease forced researchers to make more precise way to diagnose COVID-19. In the last decade, medical imaging techniques show its efficiency in helping radiologists to detect and diagnose the diseases. Deep learning and transfer learning algorithms are good techniques to detect disease from different image source types such as X-Ray and CT scan images. In this work we used a deep learning technique based on Convolution Neural Network (CNN) to detect and diagnose COVID-19 disease using Chest X-ray images. Moreover, the modified AlexNet architecture is proposed in different scenarios were differing from each other in terms of the type of the pooling layers and/or the number of the neurons that have used in the second fully connected layer. The used chest X-ray images are gathered from two COVID-19 X-ray image datasets and one dataset includes large number of normal and pneumonia X-ray images. With the proposed models we obtained the same or even better result than the original AlexNet with having a smaller number of neurons in the second fully connected layer.

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Pneumonia Detection Using Convolutional Neural Networks

International Journal for Modern Trends in Science and Technology - RTT2020 ◽

10.46501/ijmtst070117 ◽

2021 ◽

Vol 7 (01) ◽

pp. 77-80

Author(s):

Puneet Gupta

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Large Scale ◽

Disease Classification ◽

World Health ◽

X Rays ◽

X Ray ◽

Life Threatening ◽

Chest X Ray ◽

Health Organization

Abstract— Pneumonia is a life-threatening infectious disease affecting one or both lungs in humans commonly caused by bacteria called Streptococcus pneumoniae. One in three deaths in India is caused due to pneumonia as reported by World Health Organization (WHO). Chest X-Rays which are used to diagnose pneumonia, need expert radiotherapists for evaluation. Thus, developing an automatic system for detecting pneumonia would be beneficial for treating the disease without any delay particularly in remote areas. Due to the success of deep learning algorithms in analyzing medical images, Convolutional Neural Networks (CNNs) have gained much attention for disease classification. In addition, features learned by pre-trained CNN models on large-scale datasets are much useful in image classification tasks. In this work, we appraise the functionality of pre-trained CNN models utilized as feature-extractors followed by different classifiers for the classification of abnormal and normal chest X-Rays. We analytically determine the optimal CNN model for the purpose. Statistical results obtained demonstrates that pretrained CNN models employed along with supervised classifier algorithms can be very beneficial in analyzing chest X-ray images, specifically to detect Pneumonia. In this project Transfer learning and a CNN Model is used to detect whether the person has pneumonia or not using chest x-ray.

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A Deep Neural Network for Detecting Coronavirus Disease Using Chest X-Ray Images

International Journal of Healthcare Information Systems and Informatics ◽

10.4018/ijhisi.20220401.oa1 ◽

2022 ◽

Vol 17 (2) ◽

pp. 1-27

Author(s):

Rajeev Kumar Gupta ◽

Nilesh Kunhare ◽

Rajesh Kumar Pateriya ◽

Nikhlesh Pathik

Keyword(s):

Preliminary Test ◽

Vital Role ◽

World Health ◽

Test Accuracy ◽

X Rays ◽

The Novel ◽

X Ray ◽

Chest X Ray ◽

Health Organization ◽

The Comparative Study

The novel Covid-19 is one of the leading cause of death worldwide in the year 2020 and declared as a pandemic by world health organization (WHO). This virus affecting all countries across the world and 5 lakh people die as of June 2020 due to Covid-19. Due to the highly contagious nature, early detection of this virus plays a vital role to break Covid chain. Recent studies done by China says that chest CT and X-Ray image may be used as a preliminary test for Covid detection. Deep learning-based CNN model can use to detect Coronavirus automatically from the chest X-rays images. This paper proposed a transfer learning-based approach to detect Covid disease. Due to the less number of Covid chest images, we are using a pre-trained model to classify X-ray images into Covid and Normal class. This paper presents the comparative study of a various pre-trained model like VGGNet-19, ResNet50 and Inception_ResNet_V2. Experiment results show that Inception_ResNet_V2 gives the better result as compare to VGGNet and ResNet model with training and test accuracy of 99.26 and 94, respectively.

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Transfer Learning Based Multiclass Classification For Covid-19 Detection Using Chest X-Rays

10.21203/rs.3.rs-810768/v1 ◽

2021 ◽

Author(s):

Japman Singh Monga ◽

Yuvraj Singh Champawat ◽

Seema Kharb

Keyword(s):

Deep Learning ◽

Transfer Learning ◽

X Rays ◽

Performance Parameters ◽

Chain Reaction ◽

X Ray ◽

Proposed Model ◽

Chest X Ray ◽

Polymerase Chain ◽

Deep Learning Model

Abstract In the year 2020 world came to a halt due to spread of Covid-19 or SARS-CoV2 which was first identified in Wuhan, China. Since then, it has caused plethora of problems around the globe such as loss of millions of lives, economic instability etc. Less effectiveness of detection through Reverse Transcription Polymerase Chain Reaction and also prolonged time needed for detection through the same calls for a substitute for Covid-19 detection. Hence, in this study, we aim to develop a transfer learning based multi-class classifier using Chest X-Ray images which will classify the X-Ray images in 3 classes (Covid-19, Pneumonia, Normal). Further, the proposed model has been trained with deep learning classifiers namely: DenseNet201, Xception, ResNet50V2, VGG16, VGG-19, InceptionResNetV2 .These are evaluated on the basis of accuracy, precision and recall as performance parameters. It has been observed that DenseNet201 is the best deep learning model with 82.2% accuracy.

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