Triage of Potential COVID-19 Patients from Chest X-ray Images using Hierarchical Convolutional Networks

• URL: http://arxiv.org/abs/2011.00618v2
• Date: Tue, 15 Dec 2020 15:47:46 GMT
• Title: Triage of Potential COVID-19 Patients from Chest X-ray Images using Hierarchical Convolutional Networks
• Authors: Kapal Dev, Sunder Ali Khowaja, Ankur Singh Bist, Vaibhav Saini, Surbhi Bhatia
• Abstract summary: The current COVID-19 pandemic has motivated the researchers to use artificial intelligence techniques for a potential alternative to reverse transcription-polymerase chain reaction (RT-PCR) The chest X-ray (CXR) is one of the alternatives to achieve fast diagnosis but the unavailability of large-scale annotated data makes the clinical implementation of machine learning-based COVID detection difficult. In this paper, we propose the use of hierarchical convolutional network (HCN) architecture to naturally augment the data along with diversified features.
• Score: 5.7179132552879395
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: The current COVID-19 pandemic has motivated the researchers to use artificial intelligence techniques for a potential alternative to reverse transcription-polymerase chain reaction (RT-PCR) due to the limited scale of testing. The chest X-ray (CXR) is one of the alternatives to achieve fast diagnosis but the unavailability of large-scale annotated data makes the clinical implementation of machine learning-based COVID detection difficult. Another issue is the usage of ImageNet pre-trained networks which does not extract reliable feature representations from medical images. In this paper, we propose the use of hierarchical convolutional network (HCN) architecture to naturally augment the data along with diversified features. The HCN uses the first convolution layer from COVIDNet followed by the convolutional layers from well-known pre-trained networks to extract the features. The use of the convolution layer from COVIDNet ensures the extraction of representations relevant to the CXR modality. We also propose the use of ECOC for encoding multiclass problems to binary classification for improving the recognition performance. Experimental results show that HCN architecture is capable of achieving better results in comparison to the existing studies. The proposed method can accurately triage potential COVID-19 patients through CXR images for sharing the testing load and increasing the testing capacity.

Related papers

• Prototype Learning Guided Hybrid Network for Breast Tumor Segmentation in DCE-MRI [58.809276442508256]
  We propose a hybrid network via the combination of convolution neural network (CNN) and transformer layers. The experimental results on private and public DCE-MRI datasets demonstrate that the proposed hybrid network superior performance than the state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-08-11T15:46:00Z)
• Performance of GAN-based augmentation for deep learning COVID-19 image classification [57.1795052451257]
  The biggest challenge in the application of deep learning to the medical domain is the availability of training data. Data augmentation is a typical methodology used in machine learning when confronted with a limited data set. In this work, a StyleGAN2-ADA model of Generative Adversarial Networks is trained on the limited COVID-19 chest X-ray image set.
  arXiv  Detail & Related papers  (2023-04-18T15:39:58Z)
• 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)
• Preservation of High Frequency Content for Deep Learning-Based Medical Image Classification [74.84221280249876]
  An efficient analysis of large amounts of chest radiographs can aid physicians and radiologists. We propose a novel Discrete Wavelet Transform (DWT)-based method for the efficient identification and encoding of visual information.
  arXiv  Detail & Related papers  (2022-05-08T15:29:54Z)
• COVID-Net CXR-2: An Enhanced Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-ray Images [58.35627258364233]
  Use of chest X-ray (CXR) imaging as a complimentary screening strategy to RT-PCR testing continues to grow. We introduce COVID-Net CXR-2, an enhanced deep convolutional neural network design for COVID-19 detection from CXR images. benchmark dataset composed of 19,203 CXR images from a multinational cohort of 16,656 patients from at least 51 countries.
  arXiv  Detail & Related papers  (2021-05-14T04:29:21Z)
• COVID-CLNet: COVID-19 Detection with Compressive Deep Learning Approaches [0.0]
  We propose a computer-aided detection (CADe) system that uses the computed tomography (CT) scan images. This proposed boosted deep learning network (CLNet) is based on the implementation of Deep Learning (DL) networks. Experiments performed on different compressed methods show promising results for COVID-19 detection.
  arXiv  Detail & Related papers  (2020-12-03T19:56:48Z)
• Explainable-by-design Semi-Supervised Representation Learning for COVID-19 Diagnosis from CT Imaging [23.918269366873567]
  We present an explainable Deep Learning approach based on a semi-supervised classification pipeline that employs variational autoencoders to extract efficient feature embedding. With the explainable classification results, the proposed diagnosis system is very effective for COVID-19 classification.
  arXiv  Detail & Related papers  (2020-11-23T20:51:22Z)
• 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)
• A Deep Convolutional Neural Network for COVID-19 Detection Using Chest X-Rays [2.2843885788439797]
  We present image classifiers based on Dense Convolutional Networks and transfer learning to classify chest X-ray images according to three labels: COVID-19, pneumonia and normal. We were able to reach test accuracy of 100% on our test dataset.
  arXiv  Detail & Related papers  (2020-04-30T13:20:42Z)
• Automated diagnosis of COVID-19 with limited posteroanterior chest X-ray images using fine-tuned deep neural networks [4.294650528226683]
  COVID-19 is a respiratory syndrome that resembles pneumonia. Scientists, researchers and healthcare experts are contributing for faster and automated diagnosis of COVID-19 by identifying pulmonary infections. This article presents the random oversampling and weighted class loss function approach for unbiased fine-tuned learning (transfer learning) in various state-of-the-art deep learning approaches.
  arXiv  Detail & Related papers  (2020-04-23T10:24:34Z)
• COVID-MobileXpert: On-Device COVID-19 Patient Triage and Follow-up using Chest X-rays [12.100371588940256]
  COVID-MobileXpert is a lightweight deep neural network (DNN) based mobile app that can use chest X-ray (CXR) for COVID-19 case screening and radiological trajectory prediction. We employ novel loss functions and training schemes for the MS network to learn the robust features.
  arXiv  Detail & Related papers  (2020-04-06T23:43:58Z)

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.