%0 Conference Proceedings
%4 sid.inpe.br/sibgrapi/2023/08.07.20.05
%2 sid.inpe.br/sibgrapi/2023/08.07.20.05.21
%@doi 10.1109/SIBGRAPI59091.2023.10347165
%T Multimodal Convolutional Deep Belief Networks for Stroke Classification with Fourier Transform
%D 2023
%A Roder, Mateus,
%A Gomes, Nicolas,
%A Yoshida, Arissa,
%A Costen, Fumie,
%A Papa, João Paulo,
%@affiliation São Paulo State University (UNESP)
%@affiliation São Paulo State University (UNESP)
%@affiliation São Paulo State University (UNESP)
%@affiliation The University of Manchester
%@affiliation São Paulo State University (UNESP)
%E Clua, Esteban Walter Gonzalez,
%E Körting, Thales Sehn,
%E Paulovich, Fernando Vieira,
%E Feris, Rogerio,
%B Conference on Graphics, Patterns and Images, 36 (SIBGRAPI)
%C Rio Grande, RS
%8 Nov. 06-09, 2023
%S Proceedings
%K Stroke classification, Convolutional Deep Belief Network, RBM, Fourier transform.
%X 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.
%@language en
%3 roder-inpe.pdf