@InProceedings{RoderGomYosCosPap:2023:MuCoDe,
               author = "Roder, Mateus and Gomes, Nicolas and Yoshida, Arissa and Costen, 
                         Fumie and Papa, Jo{\~a}o Paulo",
          affiliation = "{S{\~a}o Paulo State University (UNESP)} and {S{\~a}o Paulo 
                         State University (UNESP)} and {S{\~a}o Paulo State University 
                         (UNESP)} and {The University of Manchester} and {S{\~a}o Paulo 
                         State University (UNESP)}",
                title = "Multimodal Convolutional Deep Belief Networks for Stroke 
                         Classification with Fourier Transform",
            booktitle = "Proceedings...",
                 year = "2023",
               editor = "Clua, Esteban Walter Gonzalez and K{\"o}rting, Thales Sehn and 
                         Paulovich, Fernando Vieira and Feris, Rogerio",
         organization = "Conference on Graphics, Patterns and Images, 36. (SIBGRAPI)",
             keywords = "Stroke classification, Convolutional Deep Belief Network, RBM, 
                         Fourier transform.",
             abstract = "Several studies have investigated the vast potential of deep 
                         learning techniques in addressing a wide range of applications, 
                         from recommendation systems and service-based analysis to medical 
                         diagnosis. However, even with the remarkable results achieved in 
                         some computer vision tasks, there is still a vast scope for 
                         exploration. Over the past decade, various studies focused on 
                         developing automated medical systems to support diagnosis. 
                         Nevertheless, detecting cerebrovascular accidents remains a 
                         challenging task. In this regard, one way to improve these 
                         approaches is to incorporate information fusion techniques in deep 
                         learning architectures. This paper proposes a novel approach to 
                         enhance stroke classification by combining multimodal data from 
                         Fourier transform with Convolutional Deep Belief Networks. As the 
                         main result, the proposed approach achieved state-of-the-art 
                         results with an accuracy of 99.94%, demonstrating its 
                         effectiveness and potential for future applications.",
  conference-location = "Rio Grande, RS",
      conference-year = "Nov. 06-09, 2023",
                  doi = "10.1109/SIBGRAPI59091.2023.10347165",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI59091.2023.10347165",
             language = "en",
                  ibi = "8JMKD3MGPEW34M/49JP76P",
                  url = "http://urlib.net/ibi/8JMKD3MGPEW34M/49JP76P",
           targetfile = "roder-inpe.pdf",
        urlaccessdate = "2024, Apr. 28"
}