Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks

Narin, Ali; Kaya, Ceren; Pamuk, Ziynet

doi:10.1007/s10044-021-00984-y

Cited by 1,403 publications

(552 citation statements)

References 47 publications

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“…In this study, we have fine-tuned existing models (VGG16, VGG19, DenseNet121, Inception-ResNet-V2, InceptionV3, Resnet50, and Xception) based on our classification requirements. These models have shown remarkable results in pneumonia detection [23][24][25] and have also been showing promising results with COVID-19 [11,26,27] classification. Hence, in this study, we have compared them based on the same data and variables to determine the best model to distinguish COVID-19 X-ray from pneumonia.…”

Section: Related Workmentioning

confidence: 97%

A comparative study of multiple neural network for detection of COVID-19 on chest X-ray

Anis

Xuan

Chuah

et al. 2021

EURASIP J. Adv. Signal Process.

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Coronavirus disease of 2019 or COVID-19 is a rapidly spreading viral infection that has affected millions all over the world. With its rapid spread and increasing numbers, it is becoming overwhelming for the healthcare workers to rapidly diagnose the condition and contain it from spreading. Hence it has become a necessity to automate the diagnostic procedure. This will improve the work efficiency as well as keep the healthcare workers safe from getting exposed to the virus. Medical image analysis is one of the rising research areas that can tackle this issue with higher accuracy. This paper conducts a comparative study of the use of the recent deep learning models (VGG16, VGG19, DenseNet121, Inception-ResNet-V2, InceptionV3, Resnet50, and Xception) to deal with the detection and classification of coronavirus pneumonia from pneumonia cases. This study uses 7165 chest X-ray images of COVID-19 (1536) and pneumonia (5629) patients. Confusion metrics and performance metrics were used to analyze each model. Results show DenseNet121 (99.48% of accuracy) showed better performance when compared with the other models in this study.

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Section: Related Workmentioning

confidence: 97%

A comparative study of multiple neural network for detection of COVID-19 on chest X-ray

Anis

Xuan

Chuah

et al. 2021

EURASIP J. Adv. Signal Process.

View full text Add to dashboard Cite

show abstract

“…Similarly, Apostolopoulos et al [24] proposed transfer learning adaption that performs CNN from scratch called Mobile Net to detect types of pneumonia. Narin et al [25] proposed 3 deep CNN models to classify a balanced dataset of x-ray images to COVID-19 or nonCOVID. Jaiswal et al [26] proposed deep learning model to identifying pneumonia types.…”

Section: Machine Learning Based Techniquesmentioning

confidence: 99%

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

Al‐Smadi

Hammad

Baker

et al. 2021

IJECE

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<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>

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“…With the rapid development of artificial intelligence, computer classification of images has been proven to have a higher accuracy rate than human eye recognition 13 (many network models have better classification effects on ImageNet data sets than ordinary people's judgment effects). Many researchers are committed to improving the detection and analysis methods of various diseases by acquiring radiology data sets and applying data science classifiers Hemdan 14 used a deep learning model to diagnose COVID-19 in X-ray images and proposed a COVIDX-Net model with 7 convolutional layers. Wang and Wong 15 proposed a deep model (COVID-Net) for COVID19 detection, which achieved 92.4% accuracy when classifying normal, non-COVID pneumonia and COVID-19.…”

Section: Related Workmentioning

confidence: 99%

A Self-Attention Mechanism Neural Network for Detection and Diagnosis of COVID-19 from Chest X-Ray Images

Cheng

Xue

Yang

et al. 2021

Preprint

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The new type of coronavirus is called COVID-19. The virus can cause respiratory diseases, accompanied by cough, fever, difficulty breathing, and in severe cases, it can also cause symptoms such as pneumonia. It began to spread at the end of 2019 and has now spread to all parts of the world. The limited test kits and increasing number of cases encourage us to propose a deep learning model that can help radiologists and clinicians use chest X-rays to detect COVID-19 cases and show the diagnostic features of pneumonia. In this study, our methods are: 1) Propose a data enhancement method to increase the diversity of the data set, thereby improving the generalization performance of the network. 2) Using the deep convolutional neural network model DPN-SE, an attention mechanism is added on the basis of the DPN network, which greatly improves the performance of the network. 3) Use the lime interpretable library to mark the X-ray, the characteristic area on the medical image that is helpful for the doctor to make a diagnosis. The model we proposed can obtain better results with the least amount of data preprocessing given limited data. In general, the proposed method and model can effectively become a very useful tool for clinical practitioners and radiologists.

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Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks

Cited by 1,403 publications

References 47 publications

A comparative study of multiple neural network for detection of COVID-19 on chest X-ray

A comparative study of multiple neural network for detection of COVID-19 on chest X-ray

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

A Self-Attention Mechanism Neural Network for Detection and Diagnosis of COVID-19 from Chest X-Ray Images

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