@InProceedings{SilvaMiraCord:2021:NeGrCr,
               author = "da Silva, Cleber Alberto Cabral Ferreira and Miranda, 
                         P{\'e}ricles Barbosa Cunha and Cordeiro, Filipe Rolim",
          affiliation = "{Federal Rural University of Pernambuco (UFRPE) } and {Federal 
                         Rural University of Pernambuco (UFRPE) } and {Federal Rural 
                         University of Pernambuco (UFRPE)}",
                title = "A New Grammar for Creating Convolutional Neural Networks Applied 
                         to Medical Image Classification",
            booktitle = "Proceedings...",
                 year = "2021",
               editor = "Paiva, Afonso and Menotti, David and Baranoski, Gladimir V. G. and 
                         Proen{\c{c}}a, Hugo Pedro and Junior, Antonio Lopes Apolinario 
                         and Papa, Jo{\~a}o Paulo and Pagliosa, Paulo and dos Santos, 
                         Thiago Oliveira and e S{\'a}, Asla Medeiros and da Silveira, 
                         Thiago Lopes Trugillo and Brazil, Emilio Vital and Ponti, Moacir 
                         A. and Fernandes, Leandro A. F. and Avila, Sandra",
         organization = "Conference on Graphics, Patterns and Images, 34. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "grammatical evolution, deep neural networks, multi-objective 
                         optimization.",
             abstract = "In the last decade, the adoption of Deep Convolutional Neural 
                         Networks (CNNs) has been successfully applied to solve computer 
                         vision tasks, such as image classification in the medical field. 
                         However, the several architectures proposed in the literature are 
                         composed of an increasing number of parameters and complexity. 
                         Therefore, finding the optimal trade-off between accuracy and 
                         model complexity for a given data set is challenging. To help the 
                         search for these suitable configurations, this work proposes using 
                         a new Context-Free Grammar associated with a Multi-Objective 
                         Grammatical Evolution Algorithm that generates suitable CNNs for a 
                         given image classification problem. In this structure, the new 
                         grammar maps every possible search space for the creation of 
                         networks. Furthermore, the Multi-Objective Grammatical Evolution 
                         Algorithm used optimizes this search taking into account two 
                         objective functions: accuracy and f1-score. Our proposal was used 
                         in three medical image datasets from MedMNIST: PathMNIST, 
                         OCTMNIST, and OrganMNIST_Axial. The results showed that our method 
                         generated simpler networks with equal or superior performance from 
                         state-of-the-art (more complex) networks and others CNNs also 
                         generated by grammatical evolution process.",
  conference-location = "Gramado, RS, Brazil (virtual)",
      conference-year = "18-22 Oct. 2021",
                  doi = "10.1109/SIBGRAPI54419.2021.00022",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI54419.2021.00022",
             language = "en",
                  ibi = "8JMKD3MGPEW34M/45BU8FB",
                  url = "http://urlib.net/ibi/8JMKD3MGPEW34M/45BU8FB",
           targetfile = "2021171880.pdf",
        urlaccessdate = "2024, May 06"
}