Efficient deep neural networks for classification of COVID-19 based on CT images: Virtualization via software defined radio

Fouladi, Saman; Ebadi, M. J.; Safaei, Ali; Bajuri, Mohd Yazid; Ahmadian, Ali

doi:10.1016/j.comcom.2021.06.011

Cited by 63 publications

(36 citation statements)

References 32 publications

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“…A deep learning method, CNN takes an input image and assigns weight to various objects in the picture, allowing it to differentiate between them. Because of its great accuracy, CNN is used to classify and identify images [ 25 ].…”

Section: Methodsmentioning

confidence: 99%

COVID-19 Detection Based on Lung Ct Scan Using Deep Learning Techniques

Kogilavani¹,

Jayagopal

Sandhiya³

et al. 2022

Computational and Mathematical Methods in Medicine

115

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SARS-CoV-2 is a novel virus, responsible for causing the COVID-19 pandemic that has emerged as a pandemic in recent years. Humans are becoming infected with the virus. In 2019, the city of Wuhan reported the first-ever incidence of COVID-19. COVID-19 infected people have symptoms that are related to pneumonia, and the virus affects the body’s respiratory organs, making breathing difficult. A real-time reverse transcriptase-polymerase chain reaction (RT-PCR) kit is used to diagnose the disease. Due to a shortage of kits, suspected patients cannot be treated promptly, resulting in disease spread. To develop an alternative, radiologists looked at the changes in radiological imaging, like CT scans, that produce comprehensive pictures of the body of excellent quality. The suspected patient’s computed tomography (CT) scan is used to distinguish between a healthy individual and a COVID-19 patient using deep learning algorithms. A lot of deep learning methods have been proposed for COVID-19. The proposed work utilizes CNN architectures like VGG16, DeseNet121, MobileNet, NASNet, Xception, and EfficientNet. The dataset contains 3873 total CT scan images with “COVID” and “Non-COVID.” The dataset is divided into train, test, and validation. Accuracies obtained for VGG16 are 97.68%, DenseNet121 is 97.53%, MobileNet is 96.38%, NASNet is 89.51%, Xception is 92.47%, and EfficientNet is 80.19%, respectively. From the obtained analysis, the results show that the VGG16 architecture gives better accuracy compared to other architectures.

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Section: Methodsmentioning

confidence: 99%

COVID-19 Detection Based on Lung Ct Scan Using Deep Learning Techniques

Kogilavani¹,

Jayagopal

Sandhiya³

et al. 2022

Computational and Mathematical Methods in Medicine

115

View full text Add to dashboard Cite

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“…Extensive research has been done today to mechanize medical images' interpretation to limit these problems. Computer-assisted image analysis can be practical in the early detection of diseases [ 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

Timely Diagnosis of Acute Lymphoblastic Leukemia Using Artificial Intelligence‐Oriented Deep Learning Methods

Rezayi

Mohammadzadeh

Bouraghi

et al. 2021

Computational Intelligence and Neuroscience

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Background. Leukemia is fatal cancer in both children and adults and is divided into acute and chronic. Acute lymphoblastic leukemia (ALL) is a subtype of this cancer. Early diagnosis of this disease can have a significant impact on the treatment of this disease. Computational intelligence-oriented techniques can be used to help physicians identify and classify ALL rapidly. Materials and Method. In this study, the utilized dataset was collected from a CodaLab competition to classify leukemic cells from normal cells in microscopic images. Two famous deep learning networks, including residual neural network (ResNet-50) and VGG-16 were employed. These two networks are already trained by our assigned parameters, meaning we did not use the stored weights; we adjusted the weights and learning parameters too. Also, a convolutional network with ten convolutional layers and 2 ∗ 2 max-pooling layers—with strides 2—was proposed, and six common machine learning techniques were developed to classify acute lymphoblastic leukemia into two classes. Results. The validation accuracies (the mean accuracy of training and test networks for 100 training cycles) of the ResNet-50, VGG-16, and the proposed convolutional network were found to be 81.63%, 84.62%, and 82.10%, respectively. Among applied machine learning methods, the lowest obtained accuracy was related to multilayer perceptron (27.33%) and highest for random forest (81.72%). Conclusion. This study showed that the proposed convolutional neural network has optimal accuracy in the diagnosis of ALL. By comparing various convolutional neural networks and machine learning methods in diagnosing this disease, the convolutional neural network achieved good performance and optimal execution time without latency. This proposed network is less complex than the two pretrained networks and can be employed by pathologists and physicians in clinical systems for leukemia diagnosis.

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“…The proposed models exhibited excellent accuracy when they were used as binary classifiers (normal and COVID-19), but their accuracy degraded when they were used as multiclass classifiers (normal, pneumonia, COVID-19). The same models (ResNet-50, VGG-16) along with auto-encoder CNN and machine learning techniques, such as nearest neighbor, logical regression, support vector machine, random forest and stochastic gradient descent, were proposed to classify CT images of COVID-19 [ 90 ]. A hybrid CNN, Sobel filter and support vector machine model was developed to detect COVID-19 based on X-ray images [ 91 ].…”

Section: Comparative Study and Discussionmentioning

confidence: 99%

Artificial Intelligence for Forecasting the Prevalence of COVID-19 Pandemic: An Overview

Elsheikh

Saba

Panchal³

et al. 2021

Healthcare

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Since the discovery of COVID-19 at the end of 2019, a significant surge in forecasting publications has been recorded. Both statistical and artificial intelligence (AI) approaches have been reported; however, the AI approaches showed a better accuracy compared with the statistical approaches. This study presents a review on the applications of different AI approaches used in forecasting the spread of this pandemic. The fundamentals of the commonly used AI approaches in this context are briefly explained. Evaluation of the forecasting accuracy using different statistical measures is introduced. This review may assist researchers, experts and policy makers involved in managing the COVID-19 pandemic to develop more accurate forecasting models and enhanced strategies to control the spread of this pandemic. Additionally, this review study is highly significant as it provides more important information of AI applications in forecasting the prevalence of this pandemic.

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Efficient deep neural networks for classification of COVID-19 based on CT images: Virtualization via software defined radio

Cited by 63 publications

References 32 publications

COVID-19 Detection Based on Lung Ct Scan Using Deep Learning Techniques

COVID-19 Detection Based on Lung Ct Scan Using Deep Learning Techniques

Timely Diagnosis of Acute Lymphoblastic Leukemia Using Artificial Intelligence‐Oriented Deep Learning Methods

Artificial Intelligence for Forecasting the Prevalence of COVID-19 Pandemic: An Overview

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