An approach to the classification of COVID-19 based on CT scans using convolutional features and genetic algorithms.

ABSTRACT: COVID-19 is a respiratory disease that, as of July 15th, 2021, has infected more than 187 million people worldwide and is responsible for more than 4 million deaths. An accurate diagnosis of COVID-19 is essential for the treatment and control of the disease. The use of computed tomography (CT) has shown to be promising for evaluating patients suspected of COVID-19 infection. The analysis of a CT examination is complex, and requires attention from a specialist. This paper presents a methodology for detecting COVID-19 from CT images. We first propose a convolutional neural network architecture to extract features from CT images, and then optimize the hyperparameters of the network using a tree Parzen estimator to choose the best parameters. Following this, we apply a selection of features us

SUBMITTER: Carvalho ED 

PROVIDER: S-EPMC8342871 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature