Multi-stage transfer learning for lung segmentation using portable X-ray devices for patients with COVID-19

• URL: http://arxiv.org/abs/2011.00133v2
• Date: Sun, 7 Mar 2021 00:29:42 GMT
• Title: Multi-stage transfer learning for lung segmentation using portable X-ray devices for patients with COVID-19
• Authors: Pl\'acido L Vidal, Joaquim de Moura, Jorge Novo, Marcos Ortega
• Abstract summary: We propose a methodology that allows to adapt the knowledge from a well-known domain with a high number of samples to a new domain with a significantly reduced number and greater complexity. We took advantage of a pre-trained segmentation model from brain magnetic resonance imaging of an unrelated pathology to obtain a robust system able to segment lung regions from portable X-ray devices.
• Score: 14.767716319266999
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: One of the main challenges in times of sanitary emergency is to quickly develop computer aided diagnosis systems with a limited number of available samples due to the novelty, complexity of the case and the urgency of its implementation. This is the case during the current pandemic of COVID-19. This pathogen primarily infects the respiratory system of the afflicted, resulting in pneumonia and in a severe case of acute respiratory distress syndrome. This results in the formation of different pathological structures in the lungs that can be detected by the use of chest X-rays. Due to the overload of the health services, portable X-ray devices are recommended during the pandemic, preventing the spread of the disease. However, these devices entail different complications (such as capture quality) that, together with the subjectivity of the clinician, make the diagnostic process more difficult and suggest the necessity for computer-aided diagnosis methodologies despite the scarcity of samples available to do so. To solve this problem, we propose a methodology that allows to adapt the knowledge from a well-known domain with a high number of samples to a new domain with a significantly reduced number and greater complexity. We took advantage of a pre-trained segmentation model from brain magnetic resonance imaging of a unrelated pathology and performed two stages of knowledge transfer to obtain a robust system able to segment lung regions from portable X-ray devices despite the scarcity of samples and lesser quality. This way, our methodology obtained a satisfactory accuracy of $0.9761 \pm 0.0100$ for patients with COVID-19, $0.9801 \pm 0.0104$ for normal patients and $0.9769 \pm 0.0111$ for patients with pulmonary diseases with similar characteristics as COVID-19 (such as pneumonia) but not genuine COVID-19.

Related papers

• Advancing Diagnostic Precision: Leveraging Machine Learning Techniques for Accurate Detection of Covid-19, Pneumonia, and Tuberculosis in Chest X-Ray Images [0.0]
  Lung diseases such as COVID-19, tuberculosis (TB), and pneumonia continue to be serious global health concerns. Paramedics and scientists are working intensively to create a reliable and precise approach for early-stage COVID-19 diagnosis.
  arXiv  Detail & Related papers  (2023-10-09T18:38:49Z)
• COVID-19 Pneumonia and Influenza Pneumonia Detection Using Convolutional Neural Networks [0.0]
  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.
  arXiv  Detail & Related papers  (2021-12-14T01:59:25Z)
• Development of a Multi-Task Learning V-Net for Pulmonary Lobar Segmentation on Computed Tomography and Application to Diseased Lungs [0.19573380763700707]
  Diseased lung regions often produce high-density zones on CT images, limiting an algorithm's execution to specify damaged lobes. This impact motivated developing an improved machine learning method to segment lung lobes. The approach can be readily adopted in the clinical setting as a robust tool for radiologists.
  arXiv  Detail & Related papers  (2021-05-11T17:10:25Z)
• COVID-19 Detection from Chest X-ray Images using Imprinted Weights Approach [67.05664774727208]
  Chest radiography is an alternative screening method for the COVID-19. Computer-aided diagnosis (CAD) has proven to be a viable solution at low cost and with fast speed. To address this challenge, we propose the use of a low-shot learning approach named imprinted weights.
  arXiv  Detail & Related papers  (2021-05-04T19:01:40Z)
• 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)
• Probabilistic combination of eigenlungs-based classifiers for COVID-19 diagnosis in chest CT images [6.1020196190084555]
  More than 100 million confirmed cases of COVID-19, including more than 2.4 million deaths. The use of medical imaging such as chest X-ray (CXR) and chest Computed Tomography (CCT) have proved to be an excellent solution. We propose an ensemble classifier based on probabilistic Support Vector Machine (SVM) in order to identify pneumonia patterns.
  arXiv  Detail & Related papers  (2021-03-04T11:30:38Z)
• Pinball-OCSVM for early-stage COVID-19 diagnosis with limited posteroanterior chest X-ray images [3.4935179780034247]
  This research proposes a novel pinball loss function based one-class support vector machine (PB-OCSVM) that can work in presence of limited COVID-19 positive CXR samples. The performance of the proposed model is compared with conventional OCSVM and existing deep learning models, and the experimental results prove that the proposed model outperformed over state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-10-16T02:34:15Z)
• M3Lung-Sys: A Deep Learning System for Multi-Class Lung Pneumonia Screening from CT Imaging [85.00066186644466]
  We propose a Multi-task Multi-slice Deep Learning System (M3Lung-Sys) for multi-class lung pneumonia screening from CT imaging. In addition to distinguish COVID-19 from Healthy, H1N1, and CAP cases, our M 3 Lung-Sys also be able to locate the areas of relevant lesions.
  arXiv  Detail & Related papers  (2020-10-07T06:22:24Z)
• Deep Learning for Automatic Pneumonia Detection [72.55423549641714]
  Pneumonia is the leading cause of death among young children and one of the top mortality causes worldwide. Computer-aided diagnosis systems showed the potential for improving diagnostic accuracy. We develop the computational approach for pneumonia regions detection based on single-shot detectors, squeeze-and-excitation deep convolution neural networks, augmentations and multi-task learning.
  arXiv  Detail & Related papers  (2020-05-28T10:54:34Z)
• 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)
• Viral Pneumonia Screening on Chest X-ray Images Using Confidence-Aware Anomaly Detection [86.81773672627406]
  Clusters of viral pneumonia during a short period of time may be a harbinger of an outbreak or pandemic, like SARS, MERS, and recent COVID-19. Rapid and accurate detection of viral pneumonia using chest X-ray can be significantly useful in large-scale screening and epidemic prevention. Viral pneumonia often have diverse causes and exhibit notably different visual appearances on X-ray images.
  arXiv  Detail & Related papers  (2020-03-27T11:32:18Z)

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.