A Survey of Machine Learning Techniques for Detecting and Diagnosing COVID-19 from Imaging

• URL: http://arxiv.org/abs/2108.04344v1
• Date: Sun, 25 Jul 2021 12:26:57 GMT
• Title: A Survey of Machine Learning Techniques for Detecting and Diagnosing COVID-19 from Imaging
• Authors: Aishwarza Panday, Muhammad Ashad Kabir, Nihad Karim Chowdhury
• Abstract summary: Due to the limited availability and high cost of the reverse transcription-polymerase chain reaction (RT-PCR) test, many studies have proposed machine learning techniques for detecting COVID-19 from medical imaging. The purpose of this study is to systematically review, assess, and synthesize research articles that have used different machine learning techniques to detect and diagnose COVID-19 from chest X-ray and CT scan images.
• Score: 1.9499120576896225
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Due to the limited availability and high cost of the reverse transcription-polymerase chain reaction (RT-PCR) test, many studies have proposed machine learning techniques for detecting COVID-19 from medical imaging. The purpose of this study is to systematically review, assess, and synthesize research articles that have used different machine learning techniques to detect and diagnose COVID-19 from chest X-ray and CT scan images. A structured literature search was conducted in the relevant bibliographic databases to ensure that the survey solely centered on reproducible and high-quality research. We selected papers based on our inclusion criteria. In this survey, we reviewed $98$ articles that fulfilled our inclusion criteria. We have surveyed a complete pipeline of chest imaging analysis techniques related to COVID-19, including data collection, pre-processing, feature extraction, classification, and visualization. We have considered CT scans and X-rays as both are widely used to describe the latest developments in medical imaging to detect COVID-19. This survey provides researchers with valuable insights into different machine learning techniques and their performance in the detection and diagnosis of COVID-19 from chest imaging. At the end, the challenges and limitations in detecting COVID-19 using machine learning techniques and the future direction of research are discussed.

Related papers

• Deep transfer learning for detecting Covid-19, Pneumonia and Tuberculosis using CXR images -- A Review [0.0]
  Review paper investigates the use of deep transfer learning techniques to detect COVID-19, pneumonia, and tuberculosis in chest X-ray images. It provides an overview of current state-of-the-art CXR image classification techniques and discusses the challenges and opportunities in applying transfer learning to this domain.
  arXiv  Detail & Related papers  (2023-03-26T02:36:47Z)
• Optimising Chest X-Rays for Image Analysis by Identifying and Removing Confounding Factors [49.005337470305584]
  During the COVID-19 pandemic, the sheer volume of imaging performed in an emergency setting for COVID-19 diagnosis has resulted in a wide variability of clinical CXR acquisitions. The variable quality of clinically-acquired CXRs within publicly available datasets could have a profound effect on algorithm performance. We propose a simple and effective step-wise approach to pre-processing a COVID-19 chest X-ray dataset to remove undesired biases.
  arXiv  Detail & Related papers  (2022-08-22T13:57:04Z)
• A review of Deep learning Techniques for COVID-19 identification on Chest CT images [0.0]
  The current COVID-19 pandemic is a serious threat to humanity that directly affects the lungs. The standard gold method for diagnosing COVID-19 is Reverse Transcription Polymerase Chain Reaction (RT-PCR) to collect swabs from affected people. Researchers have proven it to be highly effective for COVID-19 CT image classification.
  arXiv  Detail & Related papers  (2022-07-29T18:27:20Z)
• Machine learning approaches for COVID-19 detection from chest X-ray imaging: A Systematic Review [31.21638091772227]
  Machine Learning algorithms have been proposed to design support decision-making systems to assess chest X-ray images. This paper presents a systematic review of ML applied to COVID-19 detection using chest X-ray images.
  arXiv  Detail & Related papers  (2022-06-11T21:17:42Z)
• COVIDx CXR-3: A Large-Scale, Open-Source Benchmark Dataset of Chest X-ray Images for Computer-Aided COVID-19 Diagnostics [69.55060769611916]
  The use of chest X-ray (CXR) imaging as a complementary screening strategy to RT-PCR testing is increasing. Many visual perception models have been proposed for COVID-19 screening based on CXR imaging. We introduce COVIDx CXR-3, a large-scale benchmark dataset of CXR images for supporting COVID-19 computer vision research.
  arXiv  Detail & Related papers  (2022-06-08T04:39:44Z)
• COVIDx CT-3: A Large-scale, Multinational, Open-Source Benchmark Dataset for Computer-aided COVID-19 Screening from Chest CT Images [82.74877848011798]
  We introduce COVIDx CT-3, a large-scale benchmark dataset for detection of COVID-19 cases from chest CT images. COVIDx CT-3 includes 431,205 CT slices from 6,068 patients across at least 17 countries. We examine the data diversity and potential biases of the COVIDx CT-3 dataset, finding significant geographic and class imbalances.
  arXiv  Detail & Related papers  (2022-06-07T06:35:48Z)
• Resetting the baseline: CT-based COVID-19 diagnosis with Deep Transfer Learning is not as accurate as widely thought [20.66890710233269]
  Deep learning is gaining instant popularity in computer aided diagnosis of COVID-19. CT-based COVID-19 detection with visual models is currently at the forefront of medical imaging research. Our critical analysis of the literature reveals an alarming performance disparity between different published results.
  arXiv  Detail & Related papers  (2021-08-12T10:34:22Z)
• Few-shot Learning for CT Scan based COVID-19 Diagnosis [33.26861533338019]
  Coronavirus disease 2019 (COVID-19) is a Public Health Emergency of International Concern infecting more than 40 million people across 188 countries and territories. Deep learning approaches have become an effective tool for automatic screening of medical images, and it is also being considered for COVID-19 diagnosis. We propose a supervised domain adaption based COVID-19 CT diagnostic method which can perform effectively when only a small samples of labeled CT scans are available.
  arXiv  Detail & Related papers  (2021-02-01T02:37:49Z)
• 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)
• Multi-Task Driven Explainable Diagnosis of COVID-19 using Chest X-ray Images [61.24431480245932]
  COVID-19 Multi-Task Network is an automated end-to-end network for COVID-19 screening. We manually annotate the lung regions of 9000 frontal chest radiographs taken from ChestXray-14, CheXpert and a consolidated COVID-19 dataset. This database will be released to the research community.
  arXiv  Detail & Related papers  (2020-08-03T12:52:23Z)
• Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation and Diagnosis for COVID-19 [71.41929762209328]
  The pandemic of coronavirus disease 2019 (COVID-19) is spreading all over the world. Medical imaging such as X-ray and computed tomography (CT) plays an essential role in the global fight against COVID-19. The recently emerging artificial intelligence (AI) technologies further strengthen the power of the imaging tools and help medical specialists.
  arXiv  Detail & Related papers  (2020-04-06T15:21:34Z)

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.