Multimodal Convolutional Deep Belief Networks for Stroke Classification with Fourier Transform - Roder, Mateus [UNESP]; Gomes, Nicolas [UNESP]; Yoshida, Arissa [UNESP]; Papa, Joao Paulo [UNESP]; Costen, Fumie (Institute of Electrical and Electronics Engineers (IEEE), 2023)
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.