CoroDet: A deep learning based classification for COVID-19 detection using chest X-ray images

Hussain, Emtiaz; Hasan, Masud; Rahman, Anisur; Lee, Ickjai; Tamanna, Tasmi; Parvez, Mohammad Zavid

doi:10.1016/j.chaos.2020.110495

Cited by 371 publications

(239 citation statements)

References 36 publications

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“…The experimental results show that their architecture achieved an overall accuracy of 96.28%. Hussain et al (2021) presented a CoroDet, a tailored 22-layer CNN architecture to detect COVID-19 using both chest X-ray and CT modalities. The architecture consists of several layers: convolution layer, pooling layer, dense layer, flatten layer, and three activation functions.…”

Section: Related Workmentioning

confidence: 99%

An optimized transfer learning-based approach for automatic diagnosis of COVID-19 from chest x-ray images

Bahgat

Balaha

AbdulAzeem

et al. 2021

PeerJ Computer Science

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Accurate and fast detection of COVID-19 patients is crucial to control this pandemic. Due to the scarcity of COVID-19 testing kits, especially in developing countries, there is a crucial need to rely on alternative diagnosis methods. Deep learning architectures built on image modalities can speed up the COVID-19 pneumonia classification from other types of pneumonia. The transfer learning approach is better suited to automatically detect COVID-19 cases due to the limited availability of medical images. This paper introduces an Optimized Transfer Learning-based Approach for Automatic Detection of COVID-19 (OTLD-COVID-19) that applies an optimization algorithm to twelve CNN architectures to diagnose COVID-19 cases using chest x-ray images. The OTLD-COVID-19 approach adapts Manta-Ray Foraging Optimization (MRFO) algorithm to optimize the network hyperparameters’ values of the CNN architectures to improve their classification performance. The proposed dataset is collected from eight different public datasets to classify 4-class cases (COVID-19, pneumonia bacterial, pneumonia viral, and normal). The experimental result showed that DenseNet121 optimized architecture achieves the best performance. The evaluation results based on Loss, Accuracy, F1-score, Precision, Recall, Specificity, AUC, Sensitivity, IoU, and Dice values reached 0.0523, 98.47%, 0.9849, 98.50%, 98.47%, 99.50%, 0.9983, 0.9847, 0.9860, and 0.9879 respectively.

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Section: Related Workmentioning

confidence: 99%

An optimized transfer learning-based approach for automatic diagnosis of COVID-19 from chest x-ray images

Bahgat

Balaha

AbdulAzeem

et al. 2021

PeerJ Computer Science

View full text Add to dashboard Cite

show abstract

“…Deep learning techniques are giving promising results in assessing the radiological features of COVID-19 [ 61 , 62 , 63 , 64 ]. In particular, a recent study found that a proposed algorithm reached a significantly higher overall diagnostic accuracy than that obtained by a simple radiologist observation both in COVID-19 pneumonia than in pneumonia from other causes [ 65 ].…”

Section: Diagnostic Scenarios Of Sarcoidosis Patients With Sars-comentioning

confidence: 99%

“…The accuracy of classification was higher for all classes compared to the traditional method of analysis, in particular 99.1% for the two class, 94.2% for the three class, and 91.2% for the four class. The authors concluded that the CoroDet might be useful in clinical practice to predict the probability of SARS-CoV-2 infection, regardless of the results of the testing kit that could be unavailable in emergency conditions [ 61 ].…”

Section: Diagnostic Scenarios Of Sarcoidosis Patients With Sars-comentioning

confidence: 99%

Chest Imaging of Patients with Sarcoidosis and SARS-CoV-2 Infection. Current Evidence and Clinical Perspectives

et al. 2021

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The recent COVID-19 pandemic has dramatically changed the world in the last months, leading to a serious global emergency related to a novel coronavirus infection that affects both sexes of all ages ubiquitously. Advanced age, cardiovascular comorbidity, and viral load have been hypothesized as some of the risk factors for severity, but their role in patients affected with other diseases, in particular immune disorders, such as sarcoidosis, and the specific interaction between these two diseases remains unclear. The two conditions might share similar imaging findings but have distinctive features that are here described. The recent development of complex imaging softwares, called deep learning techniques, opens new scenarios for the diagnosis and management.

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“…A 22-layer CNN architecture was proposed by Hussain et al [19] for automatic COVID-19 detection using raw CXR and CT scan images. A classification accuracy of 99.1%, 94.2% and 91.2% were obtained for binary, 3-class and 4-class classification respectively.…”

Section: Related Workmentioning

confidence: 99%

A Comparative Study of Multiple Neural Network for Detection of Covid 19 on Chest X-Ray

Anis

Xuan

Chuah

et al. 2021

Preprint

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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 over-whelming for the healthcare workers ta 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 detection and classification of coronavirus pneumonia from other pneumonia cases. This study uses 7,165 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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CoroDet: A deep learning based classification for COVID-19 detection using chest X-ray images

Cited by 371 publications

References 36 publications

An optimized transfer learning-based approach for automatic diagnosis of COVID-19 from chest x-ray images

An optimized transfer learning-based approach for automatic diagnosis of COVID-19 from chest x-ray images

Chest Imaging of Patients with Sarcoidosis and SARS-CoV-2 Infection. Current Evidence and Clinical Perspectives

A Comparative Study of Multiple Neural Network for Detection of Covid 19 on Chest X-Ray

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