Deep learning techniques to process 3D chest CT

<jats:p>The idea of using X&ndash;rays and Computed Tomography (CT) images as diagnostic method has been explored in several studies. Most of these studies work with slices of CT image in 2D, requiring less computational capacity and less time to process them than 3D. The processing of volumetric data (the complete CT images in 3D) adds an extra dimension of information. However, the magnitude of the data is considerably larger than working with slices in 2D, so extra computational processing is required. In this study a model capable of performing a classification of a 3D input that represents the volume of the CT scan is proposed. The model is able to classify the 3D input between COVID&ndash;19 and Non&ndash;COVID&ndash;19, but reducing the use of resources when performing the classification. The proposed model is the <jats:italic>ResNet&ndash;50</jats:italic> model with a new dimension of information added, which is a simple <jats:italic>autoencoder</jats:italic>. This <jats:italic>autoencoder </jats:italic>is trained on the same dataset, and a vector representation of each exam is generated and used together with the exams to feed the <jats:italic>ResNet&ndash;50</jats:italic>. To validate the proposal, the same proposed model is compared with and without the <jats:italic>autoencoder </jats:italic>module that provides more information to the proposed model. The proposed model obtains better metrics than the same model without the <jats:italic>autoencoder</jats:italic>, confirming that extracting relevant features from the dataset helps improve the performance of the model.</jats:p>