Earticle

초록

영어

Voice pathology classification has become one of the primary objectives of research in biomedical engineering. This paper proposes PathoVoiceFAI, a technique that enhances the multiclass pathology classification by leveraging the power of attention layers and appropriate fusioning technique to fuse the multimodal inputs. The preliminary results show that use of mid-level fusion with attention layers improves the classification accuracy by 5% in comparison to the standard decision-level fusion technique. This highlights the effect of powerful feature extraction in enhancing the classification outcomes for application in clinical environment.

목차

Abstract
I. INTRODUCTION
II. BACKGROUND
A. Multiclass pathology classification
B. Deep learning techniques
III. PATHOVOICEFAI ARCHITECTURE
IV. EXPERIMENTAL EVALUATION
V. CONCLUSION
ACKNOWLEDGEMENT
REFERENCES

키워드

저자

• Srinidhi Kanagachalam [ Department of Electrical and Computer Engineering Inha University Incheon, South Korea ]
• Rasim Mahmudov [ Department of Electrical and Computer Engineering Inha University ]
• Deok-Hwan Kim [ Department of Electrical and Computer Engineering Inha University ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국차세대컴퓨팅학회 학술대회

• 간기

  반년간

• 수록기간

  2021~2025

• 십진분류

  KDC 566 DDC 004