COVID-19 detection from scarce chest x-ray image data using deep learning

• URL: http://arxiv.org/abs/2102.06285v1
• Date: Thu, 11 Feb 2021 22:06:03 GMT
• Title: COVID-19 detection from scarce chest x-ray image data using deep learning
• Authors: Shruti Jadon
• Abstract summary: In the current COVID-19 pandemic situation, there is an urgent need to screen infected patients quickly and accurately. Using deep learning models trained on chest X-ray images can become an efficient method for screening COVID-19 patients. Few-shot learning is a sub-field of machine learning that aims to learn the objective with less amount of data.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In the current COVID-19 pandemic situation, there is an urgent need to screen infected patients quickly and accurately. Using deep learning models trained on chest X-ray images can become an efficient method for screening COVID-19 patients in these situations. Deep learning approaches are already widely used in the medical community. However, they require a large amount of data to be accurate. The open-source community collectively has made efforts to collect and annotate the data, but it is not enough to train an accurate deep learning model. Few-shot learning is a sub-field of machine learning that aims to learn the objective with less amount of data. In this work, we have experimented with well-known solutions for data scarcity in deep learning to detect COVID-19. These include data augmentation, transfer learning, and few-shot learning, and unsupervised learning. We have also proposed a custom few-shot learning approach to detect COVID-19 using siamese networks. Our experimental results showcased that we can implement an efficient and accurate deep learning model for COVID-19 detection by adopting the few-shot learning approaches even with less amount of data. Using our proposed approach we were able to achieve 96.4% accuracy an improvement from 83% using baseline models.

Related papers

• CovidExpert: A Triplet Siamese Neural Network framework for the detection of COVID-19 [0.0]
  We develop a few-shot learning model for early detection of COVID-19 to reduce the post-effect of this dangerous disease. The proposed architecture combines few-shot learning with an ensemble of pre-trained convolutional neural networks. The suggested model achieved an overall accuracy of 98.719%, a specificity of 99.36%, a sensitivity of 98.72%, and a ROC score of 99.9% with only 200 CT scans per category for training data.
  arXiv  Detail & Related papers  (2023-02-17T17:18:02Z)
• When Accuracy Meets Privacy: Two-Stage Federated Transfer Learning Framework in Classification of Medical Images on Limited Data: A COVID-19 Case Study [77.34726150561087]
  COVID-19 pandemic has spread rapidly and caused a shortage of global medical resources. CNN has been widely utilized and verified in analyzing medical images.
  arXiv  Detail & Related papers  (2022-03-24T02:09:41Z)
• The pitfalls of using open data to develop deep learning solutions for COVID-19 detection in chest X-rays [64.02097860085202]
  Deep learning models have been developed to identify COVID-19 from chest X-rays. Results have been exceptional when training and testing on open-source data. Data analysis and model evaluations show that the popular open-source dataset COVIDx is not representative of the real clinical problem.
  arXiv  Detail & Related papers  (2021-09-14T10:59:11Z)
• A Data Augmented Approach to Transfer Learning for Covid-19 Detection [0.0]
  Covid-19 detection at an early stage can aid in an effective treatment and isolation plan to prevent its spread. Recently, transfer learning has been used for Covid-19 detection using X-ray, ultrasound, and CT scans. One of the major limitations inherent to these proposed methods is limited labeled dataset size.
  arXiv  Detail & Related papers  (2021-08-05T22:23:23Z)
• Uncertainty-Aware Semi-supervised Method using Large Unlabelled and Limited Labeled COVID-19 Data [14.530328267425638]
  We propose a Semi-supervised Classification using Limited Labelled Data (SCLLD) for automated COVID-19 detection. The proposed system is trained using 10,000 CT scans collected from Omid hospital. Our method significantly outperforms the supervised training of Convolutional Neural Network (CNN) in case labelled training data is scarce.
  arXiv  Detail & Related papers  (2021-02-12T08:20:20Z)
• FLOP: Federated Learning on Medical Datasets using Partial Networks [84.54663831520853]
  COVID-19 Disease due to the novel coronavirus has caused a shortage of medical resources. Different data-driven deep learning models have been developed to mitigate the diagnosis of COVID-19. The data itself is still scarce due to patient privacy concerns. We propose a simple yet effective algorithm, named textbfFederated textbfL textbfon Medical datasets using textbfPartial Networks (FLOP)
  arXiv  Detail & Related papers  (2021-02-10T01:56:58Z)
• A novel multiple instance learning framework for COVID-19 severity assessment via data augmentation and self-supervised learning [64.90342559393275]
  How to fast and accurately assess the severity level of COVID-19 is an essential problem, when millions of people are suffering from the pandemic around the world. We observe that there are two issues -- weak annotation and insufficient data that may obstruct automatic COVID-19 severity assessment with CT images. Our method could obtain an average accuracy of 95.8%, with 93.6% sensitivity and 96.4% specificity, which outperformed previous works.
  arXiv  Detail & Related papers  (2021-02-07T16:30:18Z)
• A Multi-Stage Attentive Transfer Learning Framework for Improving COVID-19 Diagnosis [49.3704402041314]
  We propose a multi-stage attentive transfer learning framework for improving COVID-19 diagnosis. Our proposed framework consists of three stages to train accurate diagnosis models through learning knowledge from multiple source tasks and data of different domains. Importantly, we propose a novel self-supervised learning method to learn multi-scale representations for lung CT images.
  arXiv  Detail & Related papers  (2021-01-14T01:39:19Z)
• 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)
• Experiments of Federated Learning for COVID-19 Chest X-ray Images [14.347569140079342]
  Computer vision and deep learning techniques can assist in determining COVID-19 infection with Chest X-ray Images. For the protection and respect of the privacy of patients, the hospital's specific medical-related data did not allow leakage and sharing without permission. We propose the use of federated learning for COVID-19 data training and deploy experiments to verify the effectiveness.
  arXiv  Detail & Related papers  (2020-07-05T08:25:37Z)
• Diagnosing COVID-19 Pneumonia from X-Ray and CT Images using Deep Learning and Transfer Learning Algorithms [8.697183191483571]
  COVID-19 (also known as 2019 Novel Coronavirus) first emerged in Wuhan, China and spread across the globe with unprecedented effect. This study aims to build a comprehensive dataset of X-rays and CT scan images from multiple sources. In this vein, a simple convolution neural network (CNN) and modified pre-trained AlexNet model are applied on the prepared X-rays and CT scan images dataset.
  arXiv  Detail & Related papers  (2020-03-31T18:10:10Z)

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.