An Investigation of Deep-Learned Features for Classifying Radiographic Images of COVID-19

Miguel, Pedro Lucas [UNESP]; Cansian, Adriano Mauro [UNESP]; Rozendo, Guilherme Botazzo [UNESP]; Medalha, Giuliano Cardozo; do Nascimento, Marcelo Zanchetta; Neves, Leandro Alves [UNESP]

An Investigation of Deep-Learned Features for Classifying Radiographic Images of COVID-19

Data

2023-01-01

Autores

Miguel, Pedro Lucas

Cansian, Adriano Mauro

Rozendo, Guilherme Botazzo

Medalha, Giuliano Cardozo

do Nascimento, Marcelo Zanchetta

Neves, Leandro Alves

Tipo

Trabalho apresentado em evento

Resumo

In this proposal, a study based on deep-learned features via transfer learning was developed to obtain a set of features and techniques for pattern recognition in the context of COVID-19 images. The proposal was based on the ResNet-50, DenseNet-201 and EfficientNet-b0 deep-learning models. In this work, the chosen layer for analysis was the avg pool layer from each model, with 2048 features from the ResNet-50, 1920 features from the DenseNet0201 and 1280 obtained features from the EfficientNet-b0. The most relevant descriptors were defined for the classification process, applying the ReliefF algorithm and two classification strategies: individually applied classifiers and employed an ensemble of classifiers using the score-level fusion approach. Thus, the two best combinations were identified, both using the DenseNet-201 model with the same subset of features. The first combination was defined via the SMO classifier (accuracy of 98.38%) and the second via the ensemble strategy (accuracy of 97.89%). The feature subset was composed of only 210 descriptors, representing only 10% of the original set. The strategies and information presented here are relevant contributions for the specialists interested in the study and development of computer-aided diagnosis in COVID-19 images.