Implementación de una aplicación web para el reconocimiento de patrones de diagnóstico del Covid 19 en rayos X mediante una red neuronal convolucional para la Universidad Técnica de Cotopaxi Extensión La Maná.
The implementation and development of processes through Deep Learning applied in the medicinal field is obtaining great success in several aspects that include the detection of various human diseases, so it is required to implement fast and efficient methods based on detections and evaluation of hum...
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| Формат: | bachelorThesis |
| Язык: | spa |
| Опубликовано: |
2021
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| Предметы: | |
| Online-ссылка: | http://repositorio.utc.edu.ec/handle/27000/8217 |
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Добавить метку | Итог: | The implementation and development of processes through Deep Learning applied in the medicinal field is obtaining great success in several aspects that include the detection of various human diseases, so it is required to implement fast and efficient methods based on detections and evaluation of human processes. There are great types of pulmonary diseases that cause problems to the respiratory system; one of them is SARS-CoV-2, COVID-19, whose virus can reproduce with greater intensity when there is a high concentration of person-to-person transmission. The current project aims to determine the classification of lung status within positive and negative values using a percentage of lung accuracy, using a web application with deployment of artificial intelligence models of Deep Learning type. The diagnostic sensitivity was evaluated by screening for lung damage, special metrics were established using programming tools such as TensorFlow, Keras, and Python, which allows us to apply non-maximal suppression to the calculation results of each image. Applying the DevOps methodology in the development of the web application with artificial intelligence will show us the result of the disease prediction based on the loading of an x-ray image, which artificial intelligence models deployed in a SaaS service will allow to receive the request from the client-side and return as a result the score based on a score indicating whether the lung involvement is COVID-19 positive or negative. |
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