COVID-19: Automatic Detection of the Novel Coronavirus Disease From CT Images Using an Optimized Convolutional Neural Network

Castiglione, Aniello; Vijayakumar, Pandi; Nappi, Michele; Sadiq, Saima; Umer, Muhammad

doi:10.1109/tii.2021.3057524

Cited by 84 publications

(47 citation statements)

References 28 publications

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“…The framework proposed using the model with EB0 and SVC RBF a-chieved similar performance to those obtained by the works and reported in [26,[90][91][92]. In [90] a crossvalidation with ten folds was used, as well as this work, and applied a method based on generative adversarial network (GAN), obtaining 0.9879 of F1 score.…”

Section: Assessment and Resultssupporting

confidence: 58%

“…In [26], the model with VGG-16 and xDNN achieved a performance of 0.973 in the F1 score. In [91], an optimized convolutional neural network model, called ADECO-CNN, reached 0.996 of sensitivity, 0.992 of precision, and 0.997 of specificity. In [92], a semi-supervised shallow neural network structure, called Parallel Quantum-Inspired Self-supervised Network (PQIS-Net), achieved a performance of 0.948 considering the F1 score.…”

Section: Assessment and Resultsmentioning

confidence: 99%

See 1 more Smart Citation

An Analysis of Image Features Extracted by CNNs to Design Classification Models for COVID-19 and Non-COVID-19

Teodoro

Silva

Saadi

et al. 2021

J Sign Process Syst

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Section: Assessment and Resultssupporting

confidence: 58%

Section: Assessment and Resultsmentioning

confidence: 99%

An Analysis of Image Features Extracted by CNNs to Design Classification Models for COVID-19 and Non-COVID-19

Teodoro

Silva

Saadi

et al. 2021

J Sign Process Syst

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“…Most patients have an air bronchogram 60 . The distribution characteristics of the abnormalities on X‐ray images about these five types of pneumonia are similar to those of CT images (slices) 52,61‐73 . Although the collected 2D data (e.g., X‐ray images) in our proposed data set misses lots of information (original intensity level, spacing, etc.)…”

Section: Proposed Covid‐19 Pneumonia Data Setmentioning

confidence: 75%

“…60 The distribution characteristics of the abnormalities on X-ray images about these five types of pneumonia are similar to those of CT images (slices). 52,[61][62][63][64][65][66][67][68][69][70][71][72][73] Although the collected 2D data (e.g., X-ray images) in our proposed data set misses lots of information (original intensity level, spacing, etc.) than original volume data, considering the usage of our proposed 2D-oriented algorithm, we have tried our best to keep the original size of the images while avoiding the problem of image distortion.…”

Section: Data Set Creation and Structurementioning

confidence: 99%

Learning to learn by yourself: Unsupervised meta‐learning with self‐knowledge distillation for COVID‐19 diagnosis from pneumonia cases

et al. 2021

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The goal of diagnosing the coronavirus disease 2019 (COVID‐19) from suspected pneumonia cases, that is, recognizing COVID‐19 from chest X‐ray or computed tomography (CT) images, is to improve diagnostic accuracy, leading to faster intervention. The most important and challenging problem here is to design an effective and robust diagnosis model. To this end, there are three challenges to overcome: (1) The lack of training samples limits the success of existing deep‐learning‐based methods. (2) Many public COVID‐19 data sets contain only a few images without fine‐grained labels. (3) Due to the explosive growth of suspected cases, it is urgent and important to diagnose not only COVID‐19 cases but also the cases of other types of pneumonia that are similar to the symptoms of COVID‐19. To address these issues, we propose a novel framework called Unsupervised Meta‐Learning with Self‐Knowledge Distillation to address the problem of differentiating COVID‐19 from pneumonia cases. During training, our model cannot use any true labels and aims to gain the ability of learning to learn by itself. In particular, we first present a deep diagnosis model based on a relation network to capture and memorize the relation among different images. Second, to enhance the performance of our model, we design a self‐knowledge distillation mechanism that distills knowledge within our model itself. Our network is divided into several parts, and the knowledge in the deeper parts is squeezed into the shallow ones. The final results are derived from our model by learning to compare the features of images. Experimental results demonstrate that our approach achieves significantly higher performance than other state‐of‐the‐art methods. Moreover, we construct a new COVID‐19 pneumonia data set based on text mining, consisting of 2696 COVID‐19 images (347 X‐ray + 2349 CT), 10,155 images (9661 X‐ray + 494 CT) about other types of pneumonia, and the fine‐grained labels of all. Our data set considers not only a bacterial infection or viral infection which causes pneumonia but also a viral infection derived from the influenza virus or coronavirus.

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“…Na abordagem de [Wang et al 2020b], é proposto um modelo para prever a probabilidade de infecc ¸ão da COVID-19 e encontrar lesões na TC de tórax. No trabalho proposto em [Castiglione et al 2021], é apresentada uma abordagem de pré-processamento de imagens e trazem também um modelo otimizado denominado de ADECO-CNN. Já o trabalho de [Islam and Matin 2020] apresenta o modelo LeNet-5, juntamente com a técnica de aumento de dados e um pré-processamento das imagens.…”

Section: Detecc ¸ãO Da Covid-19: Tomografias Computadorizadasunclassified

A Literature Review: Detection of COVID-19 in Computed Tomography Images Using Deep Learning

Marques¹,

Veras²,

Silva³

2021

Anais Do XIV Encontro Unificado De Computação Do Piauí (ENUCOMPI 2021)

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Through the development of the COVID-19 disease, various diagnosis methods have been studied. One of them is the computed tomography (CT), which has the best level of detail among medical image exams. The CT generates a repeatable and tiring workload, in addition to needing a team that is familiar with the findings that indicate pneumonia caused by COVID-19. To reduce this manual work and collaborate with these teams, several studies have been carried out using deep learning techniques. In this way, this study presents a review of the literature regarding the detection of COVID-19 in CT that uses deep learning to collaborate with a theoretical basis for future works.

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COVID-19: Automatic Detection of the Novel Coronavirus Disease From CT Images Using an Optimized Convolutional Neural Network

Cited by 84 publications

References 28 publications

An Analysis of Image Features Extracted by CNNs to Design Classification Models for COVID-19 and Non-COVID-19

An Analysis of Image Features Extracted by CNNs to Design Classification Models for COVID-19 and Non-COVID-19

Learning to learn by yourself: Unsupervised meta‐learning with self‐knowledge distillation for COVID‐19 diagnosis from pneumonia cases

A Literature Review: Detection of COVID-19 in Computed Tomography Images Using Deep Learning

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