Enhancing Shallow Neural Networks Through Fourier-based Information Fusion for Stroke Classification

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dc.contributor.authorRoder, Mateus [UNESP]
dc.contributor.authorRosa, Gustavo Henrique [UNESP]
dc.contributor.authorPapa, Joao Paulo [UNESP]
dc.contributor.authorCarlos, Daniel [UNESP]
dc.contributor.authorGuimaraes, [UNESP]
dc.contributor.authorPedronette, [UNESP]
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)
dc.date.accessioned2022-05-01T13:41:30Z
dc.date.available2022-05-01T13:41:30Z
dc.date.issued2021-01-01
dc.description.abstractDeep learning techniques have been widely researched and applied to several problems, ranging from recommendation systems and service-based analysis to medical diagnosis. Nevertheless, even with outstanding results in some computer vision tasks, there is still much to explore as problems are becoming more complex, or applications are demanding new restrictions that hamper current techniques performance. Several works have been developed throughout the last decade to support automated medical diagnosis, yet detecting neural-based strokes, the so-called cerebrovascular accident (CVA). However, such approaches have room for improvement, such as the employment of information fusion techniques in deep learning architectures. Such an approach might benefit CVA detection as most state-of-the-art models use computer-based tomography and magnetic resonance imaging samples. Therefore, the present work aims at enhancing stroke detection through information fusion, mainly composed of original and Fourier-based samples, applied to shallow architectures (Restricted Boltzmann machines). The whole picture employs multimodal inputs, allowing data from different domains (images and Fourier transforms) to be learned together, improving the model's predictive capacity. As the main result, the proposed approach overpassed the baselines, achieving the remarkable accuracy of 99.72%.en
dc.description.affiliationUniversidade Estadual Paulista (Unesp) Departamento de Computação
dc.description.affiliationUniversidade Estadual Paulista (Unesp) Matemática Aplicada e Computacional Departamento de Estatística
dc.description.affiliationUnespUniversidade Estadual Paulista (Unesp) Departamento de Computação
dc.description.affiliationUnespUniversidade Estadual Paulista (Unesp) Matemática Aplicada e Computacional Departamento de Estatística
dc.format.extent378-385
dc.identifierhttp://dx.doi.org/10.1109/SIBGRAPI54419.2021.00058
dc.identifier.citationProceedings - 2021 34th SIBGRAPI Conference on Graphics, Patterns and Images, SIBGRAPI 2021, p. 378-385.
dc.identifier.doi10.1109/SIBGRAPI54419.2021.00058
dc.identifier.scopus2-s2.0-85124231573
dc.identifier.urihttp://hdl.handle.net/11449/234113
dc.language.isoeng
dc.relation.ispartofProceedings - 2021 34th SIBGRAPI Conference on Graphics, Patterns and Images, SIBGRAPI 2021
dc.sourceScopus
dc.subjectFourier transform
dc.subjectRestricted Boltzmann Machines
dc.subjectStroke classification
dc.titleEnhancing Shallow Neural Networks Through Fourier-based Information Fusion for Stroke Classificationen
dc.typeTrabalho apresentado em evento
unesp.campusUniversidade Estadual Paulista (Unesp), Faculdade de Ciências, Baurupt
unesp.departmentComputação - FCpt

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