A light CNN for detecting COVID-19 from CT scans of the chest

Polsinelli, Matteo; Cinque, Luigi; Placidi, Giuseppe

doi:10.1016/j.patrec.2020.10.001

Cited by 265 publications

(157 citation statements)

References 11 publications

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“…Polsinelli et al. in [21] proposed a light CNN model based on SqueezeNet, a SOTA model, for the efficient discrimination of the COVID-19 CT scan images with the other CT scan images. Their light and efficient method helped transform the presented model’s initial concept into a more robust yet memory-efficient model.…”

Section: Related Workmentioning

confidence: 99%

Deep learning assisted COVID-19 detection using full CT-scans

et al. 2021

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Section: Related Workmentioning

confidence: 99%

Deep learning assisted COVID-19 detection using full CT-scans

et al. 2021

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“…Rahimzadeh et al [13] propose a modified version of ResNet50V2 [28] enhanced with a feature pyramid network for classifying CT scans previously selected by a hand-made selection algorithm. Polsinelli et al [14] propose a lightweight CNN architecture based on SqueezeNet [29] for discriminating between positive and negative COVID-19 images. Yang et al [11] propose a method based on multitask learning and self-supervised learning.…”

Section: A Deep Learning Based Diagnosis Of Covid-19mentioning

confidence: 99%

“…The well-trained radiologists are highly occupied during the outbreak of COVID-19, and they are unable to investigate all the received CT scans in a timely fashion. Motivated by these challenges and by the urgent need for alternatives, several artificial intelligence-based solutions for automatic diagnosis of COVID-19 from CT scans and chest radiographic data have been proposed [3], [8][9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Adversarial Graph Learning and Deep Learning Techniques for improving diagnosis within CT and Ultrasound images

Dan-Sebastian

Mitrea

Nedevschi

et al. 2020

2020 IEEE 16th International Conference on Intelligent Computer Communication and Processing (ICCP)

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Computed tomography (CT) examination has an important role in diagnosing, monitoring, and evaluating treatment response in patients infected with COVID-19. Chest CT images provide a reliable alternative to the reverse transcription polymerase chain reaction (RT-PCR). Ultrasonography provides a non-invasive and low-cost solution for diagnosing liver tumors. Recognition of hepatocellular carcinoma (HCC) in ultrasound images can provide a suitable alternative to the more invasive and dangerous method of doing a biopsy. Significant effort has been dedicated to the development of automated ways of diagnosing COVID-19 based on CT scans and on diagnosing liver tumors based on ultrasound images, some of them use deep learning methods. This paper proposes a method for training deep neural networks for performing binary classification, which is called Adversarial Graph Learning. Improved classification performance and reliability is achieved by using our method for both target tasks, recognizing COVID-19 in CT images and recognizing HCC in ultrasound images.

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“…Wang et al [4] trained the deep learning model on CT-scan images and resulted in an F1-score of 0.77. Polsinelli [5] proposed a light convolution neural network for medium specs system to diagnose COVID-19 from chest CT-scans. An accuracy, recall precision and F1score of 0.83 and 0.85, 0.817, 0.83 respectively was achieved by light CNN.…”

Section: Introductionmentioning

confidence: 99%

“…In this work, we used EfficientNet architecture and per- [3], [5], [6] and [13]. The paper is organized as follows.…”

Section: Introductionmentioning

confidence: 99%

Deep learning based diagnosis of COVID-19 using chest CT-scan images

Anwar

Zakir

2020

2020 IEEE 23rd International Multitopic Conference (INMIC)

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The Coronavirus disease (COVID-19) is an infectious disease that primarily affects lungs. This virus has spread in almost every continent. Countries are racing to slow down the spread by testing and treating patients. To diagnose the infected people, reverse transcription-polymerase chain reaction (RT-PCR) test is used. Because of colossal demand; PCR kits are under shortage, and to overcome this; radiographic techniques such as X-rays and CT-scan can be used for diagnostic purpose. In this paper, deep learning technology is used to diagnose COVID-19 in subjects through chest CT-scan. EfficientNet deep learning architecture is used for timely and accurate detection of coronavirus with an accuracy 0.897, F1 score 0.896, and AUC 0.895. Three different learning rate strategies are used, such as reducing the learning rate when model performance stops increasing (reduce on plateau), cyclic learning rate, and constant learning rate. Reduce on plateau strategy achieved F1-score of 0.9, cyclic learning rate and constant learning rate resulted in F1score of 0.86 and 0.82, respectively.

show abstract

A light CNN for detecting COVID-19 from CT scans of the chest

Cited by 265 publications

References 11 publications

Deep learning assisted COVID-19 detection using full CT-scans

Deep learning assisted COVID-19 detection using full CT-scans

Adversarial Graph Learning and Deep Learning Techniques for improving diagnosis within CT and Ultrasound images

Deep learning based diagnosis of COVID-19 using chest CT-scan images

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