COVID-19 Pneumonia and Influenza Pneumonia Detection Using Convolutional Neural Networks

• URL: http://arxiv.org/abs/2112.07102v1
• Date: Tue, 14 Dec 2021 01:59:25 GMT
• Title: COVID-19 Pneumonia and Influenza Pneumonia Detection Using Convolutional Neural Networks
• Authors: Julianna Antonchuk, Benjamin Prescott, Philip Melanchthon, Robin Singh
• Abstract summary: We developed a computer solution to support vision in differentiating between COVID-19 pneumonia, influenza virus pneumonia, and normal biomarkers. In its classification performance, the best performing model demonstrated a validation accuracy of 93% and an F1 score of 0.95.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In the research, we developed a computer vision solution to support diagnostic radiology in differentiating between COVID-19 pneumonia, influenza virus pneumonia, and normal biomarkers. The chest radiograph appearance of COVID-19 pneumonia is thought to be nonspecific, having presented a challenge to identify an optimal architecture of a convolutional neural network (CNN) that would classify with a high sensitivity among the pulmonary inflammation features of COVID-19 and non-COVID-19 types of pneumonia. Rahman (2021) states that COVID-19 radiography images observe unavailability and quality issues impacting the diagnostic process and affecting the accuracy of the deep learning detection models. A significant scarcity of COVID-19 radiography images introduced an imbalance in data motivating us to use over-sampling techniques. In the study, we include an extensive set of X-ray imaging of human lungs (CXR) with COVID-19 pneumonia, influenza virus pneumonia, and normal biomarkers to achieve an extensible and accurate CNN model. In the experimentation phase of the research, we evaluated a variety of convolutional network architectures, selecting a sequential convolutional network with two traditional convolutional layers and two pooling layers with maximum function. In its classification performance, the best performing model demonstrated a validation accuracy of 93% and an F1 score of 0.95. We chose the Azure Machine Learning service to perform network experimentation and solution deployment. The auto-scaling compute clusters offered a significant time reduction in network training. We would like to see scientists across fields of artificial intelligence and human biology collaborating and expanding on the proposed solution to provide rapid and comprehensive diagnostics, effectively mitigating the spread of the virus

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)
• COVID-Net US: A Tailored, Highly Efficient, Self-Attention Deep Convolutional Neural Network Design for Detection of COVID-19 Patient Cases from Point-of-care Ultrasound Imaging [101.27276001592101]
  We introduce COVID-Net US, a highly efficient, self-attention deep convolutional neural network design tailored for COVID-19 screening from lung POCUS images. Experimental results show that the proposed COVID-Net US can achieve an AUC of over 0.98 while achieving 353X lower architectural complexity, 62X lower computational complexity, and 14.3X faster inference times on a Raspberry Pi. To advocate affordable healthcare and artificial intelligence for resource-constrained environments, we have made COVID-Net US open source and publicly available as part of the COVID-Net open source initiative.
  arXiv  Detail & Related papers  (2021-08-05T16:47:33Z)
• 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)
• CovidGAN: Data Augmentation Using Auxiliary Classifier GAN for Improved Covid-19 Detection [6.123089440692208]
  Coronavirus (COVID-19) is a viral disease caused by severe acute respiratory syndrome coronavirus 2 (SARSCoV-2) Early results suggest that abnormalities exist in chest X-rays of patients suggestive of COVID-19. Deep learning systems like convolutional neural networks (CNNs) need a substantial amount of training data.
  arXiv  Detail & Related papers  (2021-03-08T21:53:29Z)
• FLANNEL: Focal Loss Based Neural Network Ensemble for COVID-19 Detection [61.04937460198252]
  We construct the X-ray imaging data from 2874 patients with four classes: normal, bacterial pneumonia, non-COVID-19 viral pneumonia, and COVID-19. To identify COVID-19, we propose a Focal Loss Based Neural Ensemble Network (FLANNEL) FLANNEL consistently outperforms baseline models on COVID-19 identification task in all metrics.
  arXiv  Detail & Related papers  (2020-10-30T03:17:31Z)
• RANDGAN: Randomized Generative Adversarial Network for Detection of COVID-19 in Chest X-ray [0.0]
  COVID-19 spread across the globe at an immense rate has left healthcare systems incapacitated to diagnose and test patients at the needed rate. Studies have shown promising results for detection of COVID-19 from viral bacterial pneumonia in chest X-rays. In this study, we propose a randomized generative adversarial network (RANDGAN) that detects images of an unknown class (COVID-19) without the need for labels and training data.
  arXiv  Detail & Related papers  (2020-10-06T15:58:09Z)
• 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)
• Automatic Detection of Coronavirus Disease (COVID-19) in X-ray and CT Images: A Machine Learning-Based Approach [2.488407849738164]
  COVID-19 is highly transmittable and pathogenic with no clinically approved antiviral drug or vaccine available for treatment. Medical imaging is not currently recommended in Canada for primary diagnosis of COVID-19. In this study, we compare popular deep learning-based feature extraction frameworks for automatic COVID-19 classification.
  arXiv  Detail & Related papers  (2020-04-22T15:34:45Z)
• Residual Attention U-Net for Automated Multi-Class Segmentation of COVID-19 Chest CT Images [46.844349956057776]
  coronavirus disease 2019 (COVID-19) has been spreading rapidly around the world and caused significant impact on the public health and economy. There is still lack of studies on effectively quantifying the lung infection caused by COVID-19. We propose a novel deep learning algorithm for automated segmentation of multiple COVID-19 infection regions.
  arXiv  Detail & Related papers  (2020-04-12T16:24:59Z)

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.