Uso de otimização binária de lobos cinza com deep learned features para classificar imagens radiográficas de covid-19

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dc.contributor.advisorNeves, Leandro Alves [UNESP]
dc.contributor.authorLopes, Thales Ricardo de Souza
dc.date.accessioned2023-12-05T13:18:29Z
dc.date.available2023-12-05T13:18:29Z
dc.date.issued2023-11-29
dc.description.abstractIn this work, a method based on deep learning by transfer learning is presented to perform the classification and pattern recognition in pulmonary radiographic images, representative of healthy classes and COVID-19. Thus, deep learned features from the AlexNet, Residual Neural Network, DenseNet and EfficientNet, trained on the ImageNet dataset, will be explored. The deep learned features was analyzed from different layers, such as max_pooling_3 from AlexNet, with 9216 values, avg_pool from ResNet50, with 2048 descriptors, the avg_pool of DenseNet-201, with 1920 attributes and, finally, the layer avg_pool of EfficientNet-b0, with 1280 features. The attributes was evaluated through a two-stage selection process: ranking each entry with the ReliefF algorithm and applying a threshold to reduce the number of possible combinations; application of a selection strategy wrapper, based on animal behavior, binary gray wolf optimizer, in order to find the best combinations in each subset of attributes. The discriminative power of each solution was defined by exploring ten classifiers with different heuristics. As a result, the best association occurred from the avg_pool layer of the Densenet network, SMO classifier and using only 27 attributes. This association provided an accuracy of 97.60%, an F measure of 0.976, and an AUC of 0.967. Furthermore, this solution represents a reduction of approximately 98.59% of the initial set of features which led to a higher accuracy rate when compared to the performance of the direct application of the Convolutional Neural Network with a reduced computational cost. Additionally, we believe the details presented here can contribute to the community interested in the issues explored here, supporting the development of models aimed at the diagnosis of pulmonary images of COVID-19.en
dc.description.abstractIn this work, a method based on deep learning by transfer learning is presented to perform the classification and pattern recognition in pulmonary radiographic images, representative of healthy classes and COVID-19. Thus, deep learned features from the AlexNet, Residual Neural Network, DenseNet and EfficientNet, trained on the ImageNet dataset, will be explored. The deep learned features was analyzed from different layers, such as max_pooling_3 from AlexNet, with 9216 values, avg_pool from ResNet50, with 2048 descriptors, the avg_pool of DenseNet-201, with 1920 attributes and, finally, the layer avg_pool of EfficientNet-b0, with 1280 features. The attributes was evaluated through a two-stage selection process: ranking each entry with the ReliefF algorithm and applying a threshold to reduce the number of possible combinations; application of a selection strategy wrapper, based on animal behavior, binary gray wolf optimizer, in order to find the best combinations in each subset of attributes. The discriminative power of each solution was defined by exploring ten classifiers with different heuristics. As a result, the best association occurred from the avg_pool layer of the Densenet network, SMO classifier and using only 27 attributes. This association provided an accuracy of 97.60%, an F measure of 0.976, and an AUC of 0.967. Furthermore, this solution represents a reduction of approximately 98.59% of the initial set of features which led to a higher accuracy rate when compared to the performance of the direct application of the Convolutional Neural Network with a reduced computational cost. Additionally, we believe the details presented here can contribute to the community interested in the issues explored here, supporting the development of models aimed at the diagnosis of pulmonary images of COVID-19.pt
dc.description.sponsorshipConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.description.sponsorshipId835
dc.identifier.citationLOPES, Thales Ricardo de Souza. Uso de otimização binária de lobos cinza com deep learned features para classificar imagens radiográficas de covid-19. Orientador: Leandro Alves Neves. 2023. 72 p. Trabalho de conclusão de curso (Bacharel em ciência da computação) - Ibilce - Instituto de Biociências, Letras e Ciências Exatas - Câmpus de São José do Rio Preto - Unesp, São José do Rio Preto, 2023.
dc.identifier.urihttps://hdl.handle.net/11449/251677
dc.language.isopor
dc.publisherUniversidade Estadual Paulista (Unesp)
dc.rights.accessRightsAcesso aberto
dc.subjectCOVID-19pt
dc.subjectImagens radiográficaspt
dc.subjectReconhecimento de padrõespt
dc.subjectDeep learned featuresen
dc.subjectReliefFpt
dc.subjectAlgoritmo binário de Lobos Cinzapt
dc.subjectRadiographic imagesen
dc.subjectPattern recognitionen
dc.titleUso de otimização binária de lobos cinza com deep learned features para classificar imagens radiográficas de covid-19
dc.title.alternativeUsing gray wolf binary optimization with deep learned features to classify radiographic images of covid-19en
dc.typeTrabalho de conclusão de curso
unesp.campusUniversidade Estadual Paulista (Unesp), Instituto de Biociências, Letras e Ciências Exatas, São José do Rio Preto
unesp.examinationboard.typeBanca pública
unesp.undergraduateSão José do Rio Preto - IBILCE - Ciência da Computação

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