The main purpose of this work is to investigate and compare several deep learning enhanced techniques applied to X-ray and CT-scan medical images for the detection of COVID-19. In this paper, we used four powerful pre-trained CNN models, VGG16, DenseNet121, ResNet50,and ResNet152, for the COVID-19 CT-scan binary classification task. The proposed Fast.AI ResNet framework was designed to find out the best architecture, pre-processing, and training parameters for the models largely automatically. The accuracy and F1-score were both above 96% in the diagnosis of COVID-19 using CT-scan images. In addition, we applied transfer learning techniques to overcome the insufficient data and to improve the training time. The binary and multi-class classification of X-ray images tasks were performed by utilizing enhanced VGG16 deep transfer learning architecture. High accuracy of 99% was achieved by enhanced VGG16 in the detection of X-ray images from COVID-19 and pneumonia. The accuracy and validity of the algorithms were assessed on X-ray and CT-scan well-known public datasets. The proposed methods have better results for COVID-19 diagnosis than other related in literature. In our opinion, our work can help virologists and radiologists to make a better and faster diagnosis in the struggle against the outbreak of COVID-19.

En este trabajo se determinaron los factores que influyen en la calidad de sueño de los estudiantes de ingeniería, mediante un estudio descriptivo y trasversal. Se empleó una muestra aleatoria de 930 estudiantes de ingeniería eléctrica (124), mecánica (184), civil (281), industrial (164) y química (177). Se utilizó el cuestionario auto aplicable de Índice de Calidad de Sueño de Pittsburg que valora siete componentes hipotéticos, añadiendo al análisis algunas variables socioeconómicas. Se observó que un número importante de estudiantes duermen menos de 8 horas, además se pudo confirmar que la calidad del sueño afecta el desempeño académico ocasionando necesidades médicas, también se pudo constatar que las situaciones socioeconómicas, la carrera, el sexo, la edad y el lugar de procedencia son factores determinantes en la calidad de sueño.