Loading [MathJax]/extensions/TeX/cellcolor_ieee.js
Multimodal Convolutional Deep Belief Networks for Stroke Classification with Fourier Transform | IEEE Conference Publication | IEEE Xplore

Multimodal Convolutional Deep Belief Networks for Stroke Classification with Fourier Transform

Publisher: IEEE

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...View more

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.
Date of Conference: 06-09 November 2023
Date Added to IEEE Xplore: 18 December 2023
ISBN Information:

ISSN Information:

Publisher: IEEE
Conference Location: Rio Grande, Brazil

References

References is not available for this document.