COVID-19 detection using chest computed tomography scans on Ecuadorian patients who live in the highland region.
The early detection of COVID-19 is one of the current challenges in developing effective diagnosis and treatment mechanisms for patients who are at a high risk for community contagion. Computed Tomography (CT) is an essential support for detecting the infection pattern that causes this disease. CT s...
| Autores: | , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2021 |
| País: | Ecuador |
| Institución: | Universidad de las Fuerzas Armadas |
| Repositorio: | Repositorio Universidad de las Fuerzas Armadas |
| Idioma: | inglés |
| OAI Identifier: | oai:repositorio.espe.edu.ec:21000/27324 |
| Acceso en línea: | http://repositorio.espe.edu.ec/handle/21000/27324 |
| Access Level: | acceso abierto |
| Palabra clave: | TOMOGRAFÍA COMPUTARIZADA DE TÓRAX REDES NEURONALES CONVOLUCIONALES COVID-19 APRENDIZAJE PROFUNDO. 5. SEGMENTACIÓN PULMONAR |
| Sumario: | The early detection of COVID-19 is one of the current challenges in developing effective diagnosis and treatment mechanisms for patients who are at a high risk for community contagion. Computed Tomography (CT) is an essential support for detecting the infection pattern that causes this disease. CT scans provide relevant information on the morphological appearance of the infected parenchymal tissue, known as ground-glass opacities. Artificial Intelligence (AI) can assist in the quick evaluation of CT scans to differentiate COVID-19 findings in suggestive clinical cases. In this context, AI in the form of, Convolutional Neural Networks (CNN), has achieved successful results in the analysis and classification of medical images. A deep CNN architecture is proposed in this study to diagnose COVID-19 based on the classification of Chest Computed Tomography (CCT) images. In this study 8,624 CCTs of Ecuadorian patients affected by COVID-19 in the first quarter of 2021, were examined. The initial review of CCTs was performed by medical experts to discriminate the CCTs against other chronic lung diseases not associated with COVID-19. The CCTs were pre-processed by techniques such as morphological segmentation, erosion, dilation, and adjustment. After training the model reached an overall F1-score of 97%. |
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