Multimodal Convolutional Deep Belief Networks for Stroke Classification with Fourier Transform

  • Mateus Roder UNESP
  • Nicolas Gomes UNESP
  • Arissa Yoshida UNESP
  • João Paulo Papa UNESP
  • Fumie Costen University of Manchester

Resumo


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.
Publicado
06/11/2023
RODER, Mateus; GOMES, Nicolas; YOSHIDA, Arissa; PAPA, João Paulo; COSTEN, Fumie. Multimodal Convolutional Deep Belief Networks for Stroke Classification with Fourier Transform. In: CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI), 36. , 2023, Rio Grande/RS. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2023 . p. 163-168.