UncertaintyFuseNet: Robust Uncertainty-aware Hierarchical Feature Fusion with Ensemble Monte Carlo Dropout for COVID-19 Detection

• URL: http://arxiv.org/abs/2105.08590v1
• Date: Tue, 18 May 2021 15:20:34 GMT
• Title: UncertaintyFuseNet: Robust Uncertainty-aware Hierarchical Feature Fusion with Ensemble Monte Carlo Dropout for COVID-19 Detection
• Abstract summary: COVID-19 (Coronavirus disease has infected more than 151 million people and caused approximately 3.17 million deaths around the world up to the present. We propose a novel powerful fusion model named $UncertaintyFuseNet$ that consists of an uncertainty module: Ensemble Monte Carlo (EMC) dropout. Results prove the effectiveness of our proposed fusion for COVID-19 detection using CT scan and X-Ray datasets.
• Score: 36.89349238954984
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The COVID-19 (Coronavirus disease 2019) has infected more than 151 million people and caused approximately 3.17 million deaths around the world up to the present. The rapid spread of COVID-19 is continuing to threaten human's life and health. Therefore, the development of computer-aided detection (CAD) systems based on machine and deep learning methods which are able to accurately differentiate COVID-19 from other diseases using chest computed tomography (CT) and X-Ray datasets is essential and of immediate priority. Different from most of the previous studies which used either one of CT or X-ray images, we employed both data types with sufficient samples in implementation. On the other hand, due to the extreme sensitivity of this pervasive virus, model uncertainty should be considered, while most previous studies have overlooked it. Therefore, we propose a novel powerful fusion model named $UncertaintyFuseNet$ that consists of an uncertainty module: Ensemble Monte Carlo (EMC) dropout. The obtained results prove the effectiveness of our proposed fusion for COVID-19 detection using CT scan and X-Ray datasets. Also, our proposed $UncertaintyFuseNet$ model is significantly robust to noise and performs well with the previously unseen data. The source codes and models of this study are available at: https://github.com/moloud1987/UncertaintyFuseNet-for-COVID-19-Classification.

Related papers

• A Deep Learning Approach for the Detection of COVID-19 from Chest X-Ray Images using Convolutional Neural Networks [0.0]
  COVID-19 (coronavirus) is an ongoing pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) The virus was first identified in mid-December 2019 in the Hubei province of Wuhan, China. It has spread throughout the planet with more than 75.5 million confirmed cases and more than 1.67 million deaths.
  arXiv  Detail & Related papers  (2022-01-24T21:12:25Z)
• DPCOVID: Privacy-Preserving Federated Covid-19 Detection [2.1930130356902207]
  Coronavirus (COVID-19) has shown an unprecedented global crisis by the detrimental effect on the global economy and health. We present a privacy-preserving Federated Learning system for COVID-19 detection based on chest X-ray images.
  arXiv  Detail & Related papers  (2021-10-26T15:09:00Z)
• The Report on China-Spain Joint Clinical Testing for Rapid COVID-19 Risk Screening by Eye-region Manifestations [59.48245489413308]
  We developed and tested a COVID-19 rapid prescreening model using the eye-region images captured in China and Spain with cellphone cameras. The performance was measured using area under receiver-operating-characteristic curve (AUC), sensitivity, specificity, accuracy, and F1.
  arXiv  Detail & Related papers  (2021-09-18T02:28:01Z)
• 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)
• RCoNet: Deformable Mutual Information Maximization and High-order Uncertainty-aware Learning for Robust COVID-19 Detection [12.790651338952005]
  The novel 2019 Coronavirus (COVID-19) infection has spread world widely and is currently a major healthcare challenge around the world. Due to faster imaging time and considerably lower cost than CT, detecting COVID-19 in chest X-ray (CXR) images is preferred for efficient diagnosis, assessment and treatment. We propose a novel deep network named em RCoNet$k_s$ for robust COVID-19 detection which employs em Deformable Mutual Information Maximization (DeIM), em Mixed High-order Moment Feature (MHMF) and em Multi-
  arXiv  Detail & Related papers  (2021-02-22T15:13:42Z)
• Automated Model Design and Benchmarking of 3D Deep Learning Models for COVID-19 Detection with Chest CT Scans [72.04652116817238]
  We propose a differentiable neural architecture search (DNAS) framework to automatically search for the 3D DL models for 3D chest CT scans classification. We also exploit the Class Activation Mapping (CAM) technique on our models to provide the interpretability of the results.
  arXiv  Detail & Related papers  (2021-01-14T03:45:01Z)
• 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)
• COVIDGR dataset and COVID-SDNet methodology for predicting COVID-19 based on Chest X-Ray images [0.0]
  Currently, Coronavirus disease (COVID-19) is diagnosed using RT-PCR testing, CT scans and/or Chest X-Ray (CXR) images. Deep learning neural networks have a great potential for building COVID-19 triage systems. This paper is three-fold: (i) we demystify the high sensitivities achieved by most recent COVID-19 classification models, (ii) under a close collaboration with Hospital Universitario Cl'inico San Cecilio, Granada, Spain, we built COVIDGR-1.0, and (iii) we propose COVID Smart Data based Network (COVID-SD
  arXiv  Detail & Related papers  (2020-06-02T06:18:34Z)
• Predicting COVID-19 Pneumonia Severity on Chest X-ray with Deep Learning [57.00601760750389]
  We present a severity score prediction model for COVID-19 pneumonia for frontal chest X-ray images. Such a tool can gauge severity of COVID-19 lung infections that can be used for escalation or de-escalation of care.
  arXiv  Detail & Related papers  (2020-05-24T23:13:16Z)
• CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images [0.0]
  CoroNet is a Deep Conceptional Neural Network model to automatically detect COVID-19 infection from chest X-ray images. The proposed model achieved an overall accuracy of 89.6% and the precision and recall rate for COVID-19 cases are 93% and 98.2%.
  arXiv  Detail & Related papers  (2020-04-10T07:46:07Z)

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.