Academy & Industry Research Collaboration Center (AIRCC)

Volume 12, Number 14, August 2022

System for Assistance in Diagnosis of Diseases Pulmonary


Gustavo Chichanoski and Maria Bernadete de Morais França, State University of Londrina, Brazil


Covid-19 is caused by the SARS-COV2 virus, where most people experience a mild to moderate respiratory crisis. To assist in diagnosing and triaging patients, this work developed a Covid-19 classification system through chest radiology images. For this purpose, the neural network models ResNet50V2, ResNet101V2, DenseNet121, DenseNet169, DenseNet201, InceptionResnetV2, VGG-16, and VGG-19 were used, comparing their precision, accuracy, recall, and specificity. For this, the images were segmented by a U-Net network, and packets of the lung image were generated, which served as input for the different classification models. Finally, the probabilistic Grad-CAM was generated to assist in the interpretation of the results of the neural networks. The segmentation obtained a Jaccard similarity of 94.30%, while for the classification the parameters of precision, specificity, accuracy, and revocation were evaluated, compared with the reference literature. Where DenseNet121 obtained an accuracy of 99.28%, while ResNet50V2 presented a specificity of 99.72%, both for Covid-19.


Deep Learning, U-Net, Covid-19, Segmentation & Machine Learning.