Automatic Detection of Pneumonia in Chest X-Rays using Lobe Deep Residual Network
| Název česky | Automatická detekce pneumonie v RTG snímcích hrudníku pomocí hluboké reziduální sítě Lobe |
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| Autoři | |
| Rok publikování | 2021 |
| Druh | Článek v odborném periodiku (nerecenzovaný) |
| Citace | |
| Popis | One of the critical tools for early detection and subsequent evaluation of the incidence of lung diseases is chest radiography. At a time when the speed and reliability of results, especially for COVID-19 positive patients, is important, the development of applications that would facilitate the work of untrained staff involved in the evaluation is also crucial. Our model takes the form of a simple and intuitive application, into which you only need to upload X-rays: tens or hundreds at once. In just a few seconds, the physician will determine the patient's diagnosis, including the percentage accuracy of the estimate. While the original idea was a mere binary classifier that could tell if a patient was suffering from pneumonia or not, in this paper we present a model that distinguishes between a bacterial disease, a viral infection, or a finding caused by COVID-19. The aim of this research is to demonstrate whether pneumonia can be detected or even spatially localized using a uniform, supervised classification. |