Generation of COVID-19 Chest CT Scan Images using Generative Adversarial Networks

• URL: http://arxiv.org/abs/2105.11241v1
• Date: Thu, 20 May 2021 13:04:21 GMT
• Title: Generation of COVID-19 Chest CT Scan Images using Generative Adversarial Networks
• Authors: Prerak Mann, Sahaj Jain, Saurabh Mittal, Aruna Bhat
• Abstract summary: SARS-CoV-2 is a viral contagious disease that is infected by a novel coronavirus, and has been rapidly spreading across the globe. It is very important to test and isolate people to reduce spread, and from here comes the need to do this quickly and efficiently. According to some studies, Chest-CT outperforms RT-PCR lab testing, which is the current standard, when diagnosing COVID-19 patients.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: SARS-CoV-2, also known as COVID-19 or Coronavirus, is a viral contagious disease that is infected by a novel coronavirus, and has been rapidly spreading across the globe. It is very important to test and isolate people to reduce spread, and from here comes the need to do this quickly and efficiently. According to some studies, Chest-CT outperforms RT-PCR lab testing, which is the current standard, when diagnosing COVID-19 patients. Due to this, computer vision researchers have developed various deep learning systems that can predict COVID-19 using a Chest-CT scan correctly to a certain degree. The accuracy of these systems is limited since deep learning neural networks such as CNNs (Convolutional Neural Networks) need a significantly large quantity of data for training in order to produce good quality results. Since the disease is relatively recent and more focus has been on CXR (Chest XRay) images, the available chest CT Scan image dataset is much less. We propose a method, by utilizing GANs, to generate synthetic chest CT images of both positive and negative COVID-19 patients. Using a pre-built predictive model, we concluded that around 40% of the generated images are correctly predicted as COVID-19 positive. The dataset thus generated can be used to train a CNN-based classifier which can help determine COVID-19 in a patient with greater accuracy.

Related papers

• COVID-19 Detection using Transfer Learning with Convolutional Neural Network [0.0]
  COVID-19 is a fatal infectious disease, first recognized in December 2019 in Wuhan, Hubei, China. In this study, a Transfer learning strategy (CNN) for detecting COVID-19 infection from CT images has been proposed. In the proposed model, a multilayer Convolutional neural network (CNN) with Transfer learning model Inception V3 has been designed.
  arXiv  Detail & Related papers  (2022-06-17T05:30:14Z)
• A Deep Learning Approach for the Detection of COVID-19 from Chest X-Ray Images using Convolutional Neural Networks [0.0]
  COVID-19 (coronavirus) is an ongoing pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) The virus was first identified in mid-December 2019 in the Hubei province of Wuhan, China. It has spread throughout the planet with more than 75.5 million confirmed cases and more than 1.67 million deaths.
  arXiv  Detail & Related papers  (2022-01-24T21:12:25Z)
• CNN Filter Learning from Drawn Markers for the Detection of Suggestive Signs of COVID-19 in CT Images [58.720142291102135]
  We propose a method that does not require either large annotated datasets or backpropagation to estimate the filters of a convolutional neural network (CNN) For a few CT images, the user draws markers at representative normal and abnormal regions. The method generates a feature extractor composed of a sequence of convolutional layers, whose kernels are specialized in enhancing regions similar to the marked ones.
  arXiv  Detail & Related papers  (2021-11-16T15:03:42Z)
• The Report on China-Spain Joint Clinical Testing for Rapid COVID-19 Risk Screening by Eye-region Manifestations [59.48245489413308]
  We developed and tested a COVID-19 rapid prescreening model using the eye-region images captured in China and Spain with cellphone cameras. The performance was measured using area under receiver-operating-characteristic curve (AUC), sensitivity, specificity, accuracy, and F1.
  arXiv  Detail & Related papers  (2021-09-18T02:28:01Z)
• CovidGAN: Data Augmentation Using Auxiliary Classifier GAN for Improved Covid-19 Detection [6.123089440692208]
  Coronavirus (COVID-19) is a viral disease caused by severe acute respiratory syndrome coronavirus 2 (SARSCoV-2) Early results suggest that abnormalities exist in chest X-rays of patients suggestive of COVID-19. Deep learning systems like convolutional neural networks (CNNs) need a substantial amount of training data.
  arXiv  Detail & Related papers  (2021-03-08T21:53:29Z)
• COVID-Net CT-2: Enhanced Deep Neural Networks for Detection of COVID-19 from Chest CT Images Through Bigger, More Diverse Learning [70.92379567261304]
  We introduce COVID-Net CT-2, enhanced deep neural networks for COVID-19 detection from chest CT images. We leverage explainability to investigate the decision-making behaviour of COVID-Net CT-2. Results are promising and suggest the strong potential of deep neural networks as an effective tool for computer-aided COVID-19 assessment.
  arXiv  Detail & Related papers  (2021-01-19T03:04:09Z)
• Chest X-ray Image Phase Features for Improved Diagnosis of COVID-19 Using Convolutional Neural Network [2.752817022620644]
  Recent research has shown radiography of COVID-19 patient contains salient information about the COVID-19 virus. Chest X-ray (CXR) due to its faster imaging time, wide availability, low cost and portability gains much attention. In this study, we design a novel multi-feature convolutional neural network (CNN) architecture for improved classification of COVID-19 from CXR images.
  arXiv  Detail & Related papers  (2020-11-06T20:26:26Z)
• RANDGAN: Randomized Generative Adversarial Network for Detection of COVID-19 in Chest X-ray [0.0]
  COVID-19 spread across the globe at an immense rate has left healthcare systems incapacitated to diagnose and test patients at the needed rate. Studies have shown promising results for detection of COVID-19 from viral bacterial pneumonia in chest X-rays. In this study, we propose a randomized generative adversarial network (RANDGAN) that detects images of an unknown class (COVID-19) without the need for labels and training data.
  arXiv  Detail & Related papers  (2020-10-06T15:58:09Z)
• Classification of COVID-19 in CT Scans using Multi-Source Transfer Learning [91.3755431537592]
  We propose the use of Multi-Source Transfer Learning to improve upon traditional Transfer Learning for the classification of COVID-19 from CT scans. With our multi-source fine-tuning approach, our models outperformed baseline models fine-tuned with ImageNet. Our best performing model was able to achieve an accuracy of 0.893 and a Recall score of 0.897, outperforming its baseline Recall score by 9.3%.
  arXiv  Detail & Related papers  (2020-09-22T11:53:06Z)
• COVIDNet-CT: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest CT Images [75.74756992992147]
  We introduce COVIDNet-CT, a deep convolutional neural network architecture that is tailored for detection of COVID-19 cases from chest CT images. We also introduce COVIDx-CT, a benchmark CT image dataset derived from CT imaging data collected by the China National Center for Bioinformation.
  arXiv  Detail & Related papers  (2020-09-08T15:49:55Z)
• Predicting COVID-19 Pneumonia Severity on Chest X-ray with Deep Learning [57.00601760750389]
  We present a severity score prediction model for COVID-19 pneumonia for frontal chest X-ray images. Such a tool can gauge severity of COVID-19 lung infections that can be used for escalation or de-escalation of care.
  arXiv  Detail & Related papers  (2020-05-24T23:13:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.