@InProceedings{RoderRosaPapaPedr:2021:EnShNe,
               author = "Roder, Mateus and Rosa, Gustavo Henrique and Papa, Jo{\~a}o Paulo 
                         and Pedronette, Daniel Carlos Guimar{\~a}es",
                title = "Enhancing Shallow Neural Networks Through Fourier-based 
                         Information Fusion for Stroke 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 = "Stroke classification, Restricted Boltzmann Machines, Fourier 
                         transformation.",
             abstract = "Deep learning techniques have been widely researched and applied 
                         to several problems, ranging from recommendation systems and 
                         service-based analysis to medical diagnosis. Nevertheless, even 
                         with outstanding results in some computer vision tasks, there is 
                         still much to explore as problems are becoming more complex, or 
                         applications are demanding new restrictions that hamper current 
                         techniques performance. Several works have been developed 
                         throughout the last decade to support automated medical diagnosis, 
                         yet detecting neural-based strokes, the so-called cerebrovascular 
                         accident (CVA). However, such approaches have room for 
                         improvement, such as the employment of information fusion 
                         techniques in deep learning architectures. Such an approach might 
                         benefit CVA detection as most state-of-the-art models use 
                         computer-based tomography and magnetic resonance imaging samples. 
                         Therefore, the present work aims at enhancing stroke detection 
                         through information fusion, mainly composed of original and 
                         Fourier-based samples, applied to shallow architectures 
                         (Restricted Boltzmann machines). The whole picture employs 
                         multimodal inputs, allowing data from different domains (images 
                         and Fourier transforms) to be learned together, improving the 
                         model's predictive capacity. As the main result, the proposed 
                         approach overpassed the baselines, achieving the remarkable 
                         accuracy of 99.72%.",
  conference-location = "Gramado, RS, Brazil (virtual)",
      conference-year = "18-22 Oct. 2021",
                  doi = "10.1109/SIBGRAPI54419.2021.00058",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI54419.2021.00058",
             language = "en",
                  ibi = "8JMKD3MGPEW34M/45BTS9E",
                  url = "http://urlib.net/ibi/8JMKD3MGPEW34M/45BTS9E",
           targetfile = "Paper ID 12.pdf",
        urlaccessdate = "2024, May 06"
}