DuCN: Dual-children Network for Medical Diagnosis and Similar Case Recommendation towards COVID-19

• URL: http://arxiv.org/abs/2108.01997v1
• Date: Tue, 3 Aug 2021 15:46:53 GMT
• Title: DuCN: Dual-children Network for Medical Diagnosis and Similar Case Recommendation towards COVID-19
• Authors: Chengtao Peng, Yunfei Long, Senhua Zhu, Dandan Tu, Bin Li
• Abstract summary: Early detection of coronavirus disease 2019 (COVID-19) helps to treat patients timely and increase the cure rate. We propose a novel deep learning based detection and similar case recommendation network to help control the epidemic.
• Score: 7.228593209504566
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Early detection of the coronavirus disease 2019 (COVID-19) helps to treat patients timely and increase the cure rate, thus further suppressing the spread of the disease. In this study, we propose a novel deep learning based detection and similar case recommendation network to help control the epidemic. Our proposed network contains two stages: the first one is a lung region segmentation step and is used to exclude irrelevant factors, and the second is a detection and recommendation stage. Under this framework, in the second stage, we develop a dual-children network (DuCN) based on a pre-trained ResNet-18 to simultaneously realize the disease diagnosis and similar case recommendation. Besides, we employ triplet loss and intrapulmonary distance maps to assist the detection, which helps incorporate tiny differences between two images and is conducive to improving the diagnostic accuracy. For each confirmed COVID-19 case, we give similar cases to provide radiologists with diagnosis and treatment references. We conduct experiments on a large publicly available dataset (CC-CCII) and compare the proposed model with state-of-the-art COVID-19 detection methods. The results show that our proposed model achieves a promising clinical performance.

Related papers

• Dual-Attention Residual Network for Automatic Diagnosis of COVID-19 [6.941255691176647]
  We propose a novel residual network to automatically identify COVID-19 from other common pneumonia and normal people using CT images. Our method can differentiate COVID-19 from the other two classes with 94.7% accuracy, 93.73% sensitivity, 98.28% specificity, 95.26% F1-score, and an area under the receiver operating characteristic curve (AUC) of 0.99.
  arXiv  Detail & Related papers  (2021-05-14T11:59:47Z)
• DenResCov-19: A deep transfer learning network for robust automatic classification of COVID-19, pneumonia, and tuberculosis from X-rays [5.018841080179197]
  We develop a new deep transfer learning pipeline to diagnose patients with COVID-19, pneumonia, and tuberculosis based on chest x-ray images. In our proposed model, we have created an extra layer with convolutional neural network blocks to combine these two models to establish superior performance over either model. We have tested the performance of our proposed network on two-class (pneumonia vs healthy), three-class (including COVID-19), and four-class (including tuberculosis) classification problems.
  arXiv  Detail & Related papers  (2021-04-08T18:49:22Z)
• Dual-Consistency Semi-Supervised Learning with Uncertainty Quantification for COVID-19 Lesion Segmentation from CT Images [49.1861463923357]
  We propose an uncertainty-guided dual-consistency learning network (UDC-Net) for semi-supervised COVID-19 lesion segmentation from CT images. Our proposed UDC-Net improves the fully supervised method by 6.3% in Dice and outperforms other competitive semi-supervised approaches by significant margins.
  arXiv  Detail & Related papers  (2021-04-07T16:23:35Z)
• Longitudinal Quantitative Assessment of COVID-19 Infection Progression from Chest CTs [36.71379097297172]
  We propose a new framework to identify infection at a voxel level and visualize the progression of COVID-19. In particular, we devise a longitudinal segmentation network that utilizes the reference scan information to improve the performance of disease identification.
  arXiv  Detail & Related papers  (2021-03-12T12:35:11Z)
• 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)
• Classification and Region Analysis of COVID-19 Infection using Lung CT Images and Deep Convolutional Neural Networks [0.8224695424591678]
  This work proposes a two-stage deep Convolutional Neural Networks (CNNs) based framework for delineation of COVID-19 infected regions in Lung CT images. In the first stage, COVID-19 specific CT image features are enhanced using a two-level discrete wavelet transformation. These enhanced CT images are then classified using the proposed custom-made deep CoV-CTNet. In the second stage, the CT images classified as infectious images are provided to the segmentation models for the identification and analysis of COVID-19 infectious regions.
  arXiv  Detail & Related papers  (2020-09-16T02:28:46Z)
• 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)
• CovidDeep: SARS-CoV-2/COVID-19 Test Based on Wearable Medical Sensors and Efficient Neural Networks [51.589769497681175]
  The novel coronavirus (SARS-CoV-2) has led to a pandemic. The current testing regime based on Reverse Transcription-Polymerase Chain Reaction for SARS-CoV-2 has been unable to keep up with testing demands. We propose a framework called CovidDeep that combines efficient DNNs with commercially available WMSs for pervasive testing of the virus.
  arXiv  Detail & Related papers  (2020-07-20T21:47:28Z)
• Diagnosis of Coronavirus Disease 2019 (COVID-19) with Structured Latent Multi-View Representation Learning [48.05232274463484]
  Recently, the outbreak of Coronavirus Disease 2019 (COVID-19) has spread rapidly across the world. Due to the large number of affected patients and heavy labor for doctors, computer-aided diagnosis with machine learning algorithm is urgently needed. In this study, we propose to conduct the diagnosis of COVID-19 with a series of features extracted from CT images.
  arXiv  Detail & Related papers  (2020-05-06T15:19:15Z)
• COVID-DA: Deep Domain Adaptation from Typical Pneumonia to COVID-19 [92.4955073477381]
  The outbreak of novel coronavirus disease 2019 (COVID-19) has already infected millions of people and is still rapidly spreading all over the globe. Deep learning has been used recently as effective computer-aided means to improve diagnostic efficiency. We propose a new deep domain adaptation method for COVID-19 diagnosis, namely COVID-DA.
  arXiv  Detail & Related papers  (2020-04-30T03:13:40Z)
• 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)

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.