Resumo

PRECIADO RODRIGUEZ, Adiel Joshua; FLORES GUILLEN, Flor Mayerli; SORALUZ SORALUZ, Aldo Emanuel  e  RIOS JARA, Jonathan Gerhard. Classification of Images of Pneumonia Due to Covid-19 Using Transfer Learning, Based on Convolutional Networks. RCIM [online]. 2022, vol.14, n.1  Epub 01-Jun-2022. ISSN 1684-1859.

Artificial Intelligence has helped to deal with different problems related to massive data in turn to the treatment, diagnosis and detection of diseases such as the one that currently has us in concern, Covid-19. The objective of this research has been to analyze and develop the classification of images of pneumonia due to covid-19 for an effective and optimal diagnosis. Transfer-Learning has been used applying ResNet, DenseNet, Poling and Dense layer for the elaboration of the own network models Covid-Upeu and Covid-UpeU-TL, using Kaggle and Google colab platforms, where 4 experiments have been carried out. The result with a better classification of images was obtained in experiment 4 test N ° 2 with the Covid-UPeU-TL model where Acc.Train: 0.9664 and Acc.Test: 0.9851. The implemented models have been developed with the purpose of having a holistic view of the factors for optimization in the classification of COVID-19 images.

Palavras-chave : COVID-19; Transfer-Learnig; Recognition; Artificial intelligence; Pandemic; X-rays; Image classification; Lungs; convolutional networks.

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