ArticleExpert systems with applications2021
Multi-stage transfer learning for lung segmentation using portable X-ray devices for patients with COVID-19.
Article in Expert systems with applications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Automated COVID-19 diagnosis and prognosis with medical imaging and who is publishing: a systematic review.Physical and engineering sciences in medicine · 2022Pooled it
- Development and evaluation of a multistage transfer learning framework for robust medical image analysis.Scientific reports · 2026Article
- Spatial Attention-guided Hybrid Deep Learning with Sharpened Cosine Similarity for Accurate Chest X-ray InterpretationCurrent medical imaging · 2026Article
- Lung Segmentation with Lightweight Convolutional Attention Residual U-Net.Diagnostics (Basel, Switzerland) · 2025Article
- CXR-Seg: A Novel Deep Learning Network for Lung Segmentation from Chest X-Ray Images.Bioengineering (Basel, Switzerland) · 2025Article
- Efficient clinical decision-making process via AI-based multimodal data fusion: A COVID-19 case study.Heliyon · 2024Article
- A tree-based explainable AI model for early detection of Covid-19 using physiological data.BMC medical informatics and decision making · 2024Article
- Comprehensive analysis of clinical data for COVID-19 outcome estimation with machine learning models.Biomedical signal processing and control · 2023Article
- Application of Machine Learning and Deep Learning Techniques for COVID-19 Screening Using Radiological Imaging: A Comprehensive Review.SN computer science · 2023Article
- Development of lung segmentation method in x-ray images of children based on TransResUNet.Frontiers in radiology · 2023Article
- Vision Transformers for Lung Segmentation on CXR Images.SN computer science · 2023Article
- Development of ultra-thin radiation-shielding paper through nanofiber modeling of morpho butterfly wing structure.Scientific reports · 2022Article
- Application of Two-Dimensional Entropy Measures to Detect the Radiographic Signs of Tooth Resorption and Hypercementosis in an Equine Model.Biomedicines · 2022Article
- Lesion segmentation in lung CT scans using unsupervised adversarial learning.Medical & biological engineering & computing · 2022Article
- An Unsupervised Fuzzy Clustering Approach for Early Screening of COVID-19 From Radiological Images.IEEE transactions on fuzzy systems : a publication of the IEEE Neural Networks Council · 2022Article
- Automatic lung segmentation in chest X-ray images using improved U-Net.Scientific reports · 2022Article
- Does imbalance in chest X-ray datasets produce biased deep learning approaches for COVID-19 screening?BMC medical research methodology · 2022Article
- A novel unsupervised approach based on the hidden features of Deep Denoising Autoencoders for COVID-19 disease detection.Expert systems with applications · 2022Article
- Patchless Multi-Stage Transfer Learning for Improved Mammographic Breast Mass Classification.Cancers · 2022Article
- A Novel Multistage Transfer Learning for Ultrasound Breast Cancer Image Classification.Diagnostics (Basel, Switzerland) · 2022Article
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4 authors.
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No grant is acknowledged in the PubMed record.
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
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