Detection of COVID-19 Disease using Deep Neural Networks with Ultrasound Imaging

• URL: http://arxiv.org/abs/2104.01509v1
• Date: Sun, 4 Apr 2021 00:53:06 GMT
• Title: Detection of COVID-19 Disease using Deep Neural Networks with Ultrasound Imaging
• Authors: Carlos Rojas-Azabache, Karen Vilca-Janampa, Renzo Guerrero-Huayta, Dennis N\'u\~nez-Fern\'andez
• Abstract summary: The new coronavirus 2019 has rapidly become a pandemic and has had a devastating effect on both everyday life and public health. It is critical to detect positive cases as early as possible to prevent the further spread of this epidemic and to treat affected patients quickly. This paper proposes the analysis of images of lung ultrasound scans using a convolutional neural network.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The new coronavirus 2019 (COVID-2019) has rapidly become a pandemic and has had a devastating effect on both everyday life, public health and the global economy. It is critical to detect positive cases as early as possible to prevent the further spread of this epidemic and to treat affected patients quickly. The need for auxiliary diagnostic tools has increased as accurate automated tool kits are not available. This paper presents a work in progress that proposes the analysis of images of lung ultrasound scans using a convolutional neural network. The trained model will be used on a Raspberry Pi to predict on new images.

Related papers

• Evaluating Convolutional Neural Networks for COVID-19 classification in chest X-ray images [0.31457219084519006]
  The effective screening of infected patients is a critical step to struggle with COVID-19. Recent researches using chest X-ray images suggest they include relevant information about the COVID-19 virus. This paper presents a method for automatic COVID-19 detection using chest Xray images through four convolutional neural networks.
  arXiv  Detail & Related papers  (2024-12-26T22:05:30Z)
• COVID-Net USPro: An Open-Source Explainable Few-Shot Deep Prototypical Network to Monitor and Detect COVID-19 Infection from Point-of-Care Ultrasound Images [66.63200823918429]
  COVID-Net USPro monitors and detects COVID-19 positive cases with high precision and recall from minimal ultrasound images. The network achieves 99.65% overall accuracy, 99.7% recall and 99.67% precision for COVID-19 positive cases when trained with only 5 shots.
  arXiv  Detail & Related papers  (2023-01-04T16:05:51Z)
• 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)
• Implementing a Detection System for COVID-19 based on Lung Ultrasound Imaging and Deep Learning [0.0]
  The COVID-19 pandemic started in China in December 2019 and quickly spread to several countries. To achieve large-scale control of this pandemic, fast tools for detection and treatment of patients are needed. In this paper we present the ongoing work on a system for COVID-19 detection using ultrasound imaging and using Deep Learning techniques.
  arXiv  Detail & Related papers  (2021-06-20T08:33:33Z)
• Wide & Deep neural network model for patch aggregation in CNN-based prostate cancer detection systems [51.19354417900591]
  Prostate cancer (PCa) is one of the leading causes of death among men, with almost 1.41 million new cases and around 375,000 deaths in 2020. To perform an automatic diagnosis, prostate tissue samples are first digitized into gigapixel-resolution whole-slide images. Small subimages called patches are extracted and predicted, obtaining a patch-level classification.
  arXiv  Detail & Related papers  (2021-05-20T18:13:58Z)
• COVID-19 Detection from Chest X-ray Images using Imprinted Weights Approach [67.05664774727208]
  Chest radiography is an alternative screening method for the COVID-19. Computer-aided diagnosis (CAD) has proven to be a viable solution at low cost and with fast speed. To address this challenge, we propose the use of a low-shot learning approach named imprinted weights.
  arXiv  Detail & Related papers  (2021-05-04T19:01:40Z)
• In-Line Image Transformations for Imbalanced, Multiclass Computer Vision Classification of Lung Chest X-Rays [91.3755431537592]
  This study aims to leverage a body of literature in order to apply image transformations that would serve to balance the lack of COVID-19 LCXR data. Deep learning techniques such as convolutional neural networks (CNNs) are able to select features that distinguish between healthy and disease states. This study utilizes a simple CNN architecture for high-performance multiclass LCXR classification at 94 percent accuracy.
  arXiv  Detail & Related papers  (2021-04-06T02:01:43Z)
• Deep Neural Networks for COVID-19 Detection and Diagnosis using Images and Acoustic-based Techniques: A Recent Review [0.36550217261503676]
  The new coronavirus disease (COVID-19) has been declared a pandemic since March 2020 by the World Health Organization. It consists of an emerging viral infection with respiratory tropism that could develop atypical pneumonia. Experts emphasize the importance of early detection of those who have the COVID-19 virus.
  arXiv  Detail & Related papers  (2020-12-10T19:52:12Z)
• Momentum Contrastive Learning for Few-Shot COVID-19 Diagnosis from Chest CT Images [41.73507451077361]
  We propose a new deep learning algorithm for the automated diagnosis of COVID-19, which only requires a few samples for training. We use contrastive learning to train an encoder which can capture expressive feature representations on large and publicly available lung datasets. Our results demonstrate the superior performance of our model for the accurate diagnosis of COVID-19 based on chest CT images.
  arXiv  Detail & Related papers  (2020-06-16T10:14:58Z)
• Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Images [152.34988415258988]
  Automated detection of lung infections from computed tomography (CT) images offers a great potential to augment the traditional healthcare strategy for tackling COVID-19. segmenting infected regions from CT slices faces several challenges, including high variation in infection characteristics, and low intensity contrast between infections and normal tissues. To address these challenges, a novel COVID-19 Deep Lung Infection Network (Inf-Net) is proposed to automatically identify infected regions from chest CT slices.
  arXiv  Detail & Related papers  (2020-04-22T07:30:56Z)
• 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.