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Data Augmentation Approaches Using Cycle-Consistent Adversarial Networks for Improving COVID-19 Screening in Portable Chest X-Ray Images
(Elsevier, 2021)
[Abstract] The current COVID-19 pandemic, that has caused more than 100 million cases as well as more than two million deaths worldwide, demands the development of fast and accurate diagnostic methods despite the lack of ...
Multi-Stage Transfer Learning for Lung Segmentation Using Portable X-Ray Devices for Patients With COVID-19
(Elsevier BV, 2021-07)
[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 ...
COVID-19 Lung Radiography Segmentation by Means of Multiphase Transfer Learning
(MDPI, 2021)
[Abstract] COVID-19 is characterized by its impact on the respiratory system and, during the global outbreak of 2020, specific protocols had to be designed to contain its spread within hospitals. This required the use of ...
Portable Chest X-ray Synthetic Image Generation for the COVID-19 Screening
(MDPI, 2021)
[Abstract] The global pandemic of COVID-19 raises the importance of having fast and reliable methods to perform an early detection and to visualize the evolution of the disease in every patient, which can be assessed with ...
Computational Radiological Screening of Patients with COVID-19 Using Chest X-ray Images from Portable Devices
(MDPI, 2021)
[Abstract] This work presents a fully automatic system for the screening of chest X-ray images from portable devices under the analysis of three different clinical categories: normal, pathological cases of pulmonary diseases ...