COV-RadNet: A Deep Convolutional Neural Network for Automatic Detection of COVID-19 from Chest X-Rays and CT Scans

Islam, Md. Khairul; Habiba, Sultana Umme; Khan, Tahsin Ahmed; Tasnim, Farzana

doi:10.1016/j.cmpbup.2022.100064

Cited by 8 publications

(8 citation statements)

References 24 publications

(28 reference statements)

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“…However, the fact that the data set used in this study is much larger and different data sets are used are thought to be the main reasons underlying this difference in success. Using the same dataset, Islam et al [29] obtained more successful results in their study (Tablo 7). However, in their study, they performed balanced analyses by equalizing the number of class-based instances of this dataset, which contains different instances for each class.…”

Section: Discussionmentioning

confidence: 84%

“…In this study, we propose a hybrid artificial intelligence system, DeepFeat-E, based on deep features extracted from X-Ray images using pre-trained TL models and an ensemble learning structure in which these features are processed by five selected best classical ML models. Although there are many artificial intelligence systems based on deep networks for COVID-19 diagnosis in the literature, this study differs from them since it is based on ensemble learning methods [6,15,[25][26][27][28][29]. On the other hand, although there are similar studies using ensemble classifiers in the literature, it is seen that there are various studies in which snapshots of the same convolutional neural network or TL model during the training process are used as separate classifiers in the ensemble classifier [19][20][21][22][23][24].…”

Section: Discussionmentioning

confidence: 97%

“…Islam et al [29] proposed a new convolutional neural network model called Cov-RADNet for the diagnosis of COVID-19 from CT and X-ray images. The first dataset used in this study consists of X-ray images with four categories: COVID-19, viral pneumonia, lung-opacity, and normal.…”

Section: Related Workmentioning

confidence: 99%

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A Hybrid Model Based on Deep Features and Ensemble Learning for the Diagnosis of COVID-19: DeepFeat-E

ÖZAYDIN

Tekin

2023

Turkish Journal of Science and Technology

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COVID-19, which has been declared a pandemic disease, has affected the lives of millions of people and caused a major epidemic. Despite the development of vaccines and vaccination to prevent the transmission of the disease, COVID-19 case rates fluctuate worldwide. Therefore, rapid and reliable diagnosis of COVID-19 disease is of critical importance. For this purpose, a hybrid model based on transfer learning methods and ensemble classifiers is proposed in this study. In this hybrid approach, called DeepFeat-E, the diagnosis process is performed by using deep features obtained from transfer learning models and ensemble classifiers consisting of classical machine learning methods. To test the proposed approach, a dataset of 21,165 X-ray images including 10,192 Normal, 6012 Lung Opacity, 1345 Viral Pneumonia and 3616 COVID-19 were used. With the proposed approach, the highest accuracy was achieved with the deep features of the DenseNet201 transfer learning model and the Stacking ensemble learning method. Accordingly, the test accuracy was 90.17%, 94.99% and 94.93% for four, three and two class applications, respectively. According to the results obtained in this study, it is seen that the proposed hybrid system can be used quickly and reliably in the diagnosis of COVID-19 and lower respiratory tract infections.

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Section: Discussionmentioning

confidence: 84%

Section: Discussionmentioning

confidence: 97%

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A Hybrid Model Based on Deep Features and Ensemble Learning for the Diagnosis of COVID-19: DeepFeat-E

ÖZAYDIN

Tekin

2023

Turkish Journal of Science and Technology

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“…In all scenarios except one, they achieved more than 90 percent accuracy, and they also succeeded in separating COVID-19 from normal with 99% accuracy. By analyzing chest X-rays and CT scans, authors of [36] proposed a deep convolutional neural network, "COV-RadNet," to detect COVID-19, viral pneumonia, lung opacity, and normal, healthy people. In the four-class classification, they achieved 97% accuracy.…”

Section: Related Workmentioning

confidence: 99%

FirecovNet: A Novel, Lightweight, and Fast Deep Learning-Based Network for Detecting COVID-19 Patients Using Chest X-rays

et al. 2022

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At the end of 2019, a new virus (SARS-CoV-2) called COVID-19 was reported in Wuhan, China, and spread rapidly worldwide. After two years later, several variants of this virus were created, infecting 608 million people and causing 6.51 million deaths. Due to the insufficient sensitivity of RT-PCR test kits, one of the main tools for detecting the virus, chest X-ray images are a popular tool for diagnosing the virus in patients with respiratory symptoms. Models based on deep learning are showing promising results in combating this pandemic. A novel convolutional neural network, FirecovNet, is suggested in this study that detects COVID-19 infection automatically based on raw chest X-ray images. With an architecture inspired by the integration of DarkNet and SqueezeNet networks, the proposed model has fewer parameters than state-of-the-art models and is trained using COVID-19, bacterial pneumonia, normal, lung opacity, and viral pneumonia images, which were collected from two public datasets and also are symmetric in the distribution in class. FirecovNet performance has been verified using the stratified 5-fold cross-validation method. A total of five classification tasks are performed, including four 4-class classifications, and one 5-class classification, and the accuracy of all tasks was at least 95.9%. For all classification tasks, the proposed network has demonstrated promising results in precision, sensitivity, and F1-score. Moreover, a comparison was made between the proposed network and eight deep transfer learning networks and in terms of accuracy, precision, sensitivity, F1-score, speed, and size of the saved model; FirecovNet was very promising. Therefore, FirecovNet can be useful as a tool for more accurate diagnosis of the COVID-19 virus, along with diagnostic tests, in situations where the number of specialist radiologists may be limited.

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“…The fight against COVID-19 motivated many scientific institutions and researchers of all specialties around the world to search for effective methods and techniques that would help put an end to this pandemic. In that direction, the computer vision community did not lag behind and many papers were published to address this disease, using mainly X-ray and CT images [3][4][5]. In this sense, many researches have proven that chest computed tomography was more effective and sensitive in detecting COVID-19 than RT-PCR tests [6].…”

Section: Introductionmentioning

confidence: 99%

An Experimental Study for Automatic Detection of COVID-19 from Chest CT Scans. A Comparison through Deep Learning and Support Vector Machine

Rodriguez,

Leon,

Brito

et al. 2024

Preprint

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COVID-19 was a terrible worldwide pandemic that caused a global public health crisis, with numerous deaths and a severe economic depression. To suppress the spread of COVID-19 and initiate patient isolation and contact tracing to reduce its effect, early diagnosis was required, with real-time reverse transcriptase polymerase chain reaction (RT-PCR) swab test being the most widely used. However, the RT-PCR test was sometimes found to be lengthy and inaccurate and; for that reason, in many cases of severe complications of COVID-19 pneumonia, chest screening with radiography imaging was preferred. In this paper, we carried out a study for COVID-19 detection from computed tomography images, and compared the obtained results using deep learning (DL) and support vector machine (SVM). The findings showed that, despite the excellent results shown by deep learning, the accuracy in predicting COVID-19 using SVM, in the presence of small databases, was slightly higher than DL. The execution time using SVM was also shorter

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COV-RadNet: A Deep Convolutional Neural Network for Automatic Detection of COVID-19 from Chest X-Rays and CT Scans

Cited by 8 publications

References 24 publications

A Hybrid Model Based on Deep Features and Ensemble Learning for the Diagnosis of COVID-19: DeepFeat-E

A Hybrid Model Based on Deep Features and Ensemble Learning for the Diagnosis of COVID-19: DeepFeat-E

FirecovNet: A Novel, Lightweight, and Fast Deep Learning-Based Network for Detecting COVID-19 Patients Using Chest X-rays

An Experimental Study for Automatic Detection of COVID-19 from Chest CT Scans. A Comparison through Deep Learning and Support Vector Machine

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