Diseño de una aplicación para detectar covid-19 mediante redes neuronales convolucionales e imágenes de rayos X

This research presents the design of an application to detect covid-19 using convolutional neural networks and X-ray images in two scenarios (covid/Non-covid and covid/Normal/Pneumonia). To avoid overfitting online data augmentation, dropout, batch normalization, and Adam optimizer was used. The three-class network was used as a pre-trained model, tuning only the dense and output layers to obtain the binary model. Additionally, hyper-parameter optimization was used to get dropout probabilities, activation functions, and neurons. The learning rate was adjusted using callbacks to avoid local optimums. Networks were converted to TensorFlow.js format and embedded locally in a hybrid application using Ionic and Capacitor and were deployed through Firebase to help provide diagnostics. The application obtained an accuracy of 98.61% and 96.48% for two and three classes, respectively, achieving higher performance when compared to other proposals (offline models) in the literature and using fewer training parameters.