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		<identifier>8JMKD3MGPEW34M/49JP76P</identifier>
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		<doi>10.1109/SIBGRAPI59091.2023.10347165</doi>
		<citationkey>RoderGomYosCosPap:2023:MuCoDe</citationkey>
		<title>Multimodal Convolutional Deep Belief Networks for Stroke Classification with Fourier Transform</title>
		<format>On-line</format>
		<year>2023</year>
		<numberoffiles>1</numberoffiles>
		<size>877 KiB</size>
		<author>Roder, Mateus,</author>
		<author>Gomes, Nicolas,</author>
		<author>Yoshida, Arissa,</author>
		<author>Costen, Fumie,</author>
		<author>Papa, João Paulo,</author>
		<affiliation>São Paulo State University (UNESP)</affiliation>
		<affiliation>São Paulo State University (UNESP)</affiliation>
		<affiliation>São Paulo State University (UNESP)</affiliation>
		<affiliation>The University of Manchester</affiliation>
		<affiliation>São Paulo State University (UNESP)</affiliation>
		<editor>Clua, Esteban Walter Gonzalez,</editor>
		<editor>Körting, Thales Sehn,</editor>
		<editor>Paulovich, Fernando Vieira,</editor>
		<editor>Feris, Rogerio,</editor>
		<e-mailaddress>mateus.roder@unesp.br</e-mailaddress>
		<conferencename>Conference on Graphics, Patterns and Images, 36 (SIBGRAPI)</conferencename>
		<conferencelocation>Rio Grande, RS</conferencelocation>
		<date>Nov. 06-09, 2023</date>
		<booktitle>Proceedings</booktitle>
		<tertiarytype>Full Paper</tertiarytype>
		<transferableflag>1</transferableflag>
		<keywords>Stroke classification, Convolutional Deep Belief Network, RBM, Fourier transform.</keywords>
		<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.</abstract>
		<language>en</language>
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