An Efficient SMOTE-Based Deep Learning Model for Voice Pathology Detection

被引:8
|
作者
Lee, Ji-Na [1 ]
Lee, Ji-Yeoun [2 ]
机构
[1] Seokyeong Univ, Div Global Business Languages, Seoul 02173, South Korea
[2] Eulji Univ, Dept Bigdata Med Convergence, 553 Sanseong daero, Seongnam Si 13135, South Korea
来源
APPLIED SCIENCES-BASEL | 2023年 / 13卷 / 06期
基金
新加坡国家研究基金会;
关键词
pathological voice; disordered voice; imbalanced learning; voice pathology classification; SMOTE; ADASYN; Borderline-SMOTE; deep learning; intelligent medical diagnosis system; DISEASE DETECTION; IMBALANCED DATA;
D O I
10.3390/app13063571
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
The Saarbruecken Voice Database (SVD) is a public database used by voice pathology detection systems. However, the distributions of the pathological and normal voice samples show a clear class imbalance. This study aims to develop a system for the classification of pathological and normal voices that uses efficient deep learning models based on various oversampling methods, such as the adaptive synthetic sampling (ADASYN), synthetic minority oversampling technique (SMOTE), and Borderline-SMOTE directly applied to feature parameters. The suggested combinations of oversampled linear predictive coefficients (LPCs), mel-frequency cepstral coefficients (MFCCs), and deep learning methods can efficiently classify pathological and normal voices. The balanced datasets from ADASYN, SMOTE, and Borderline-SMOTE are used to validate and evaluate the various deep learning models. The experiments are conducted using model evaluation metrics such as the recall, specificity, G, and F1 value. The experimental results suggest that the proposed voice pathology detection (VPD) system integrating the LPCs oversampled by the SMOTE and a convolutional neural network (CNN) can effectively yield the highest accuracy at 98.89% when classifying pathological and normal voices. Finally, the performances of oversampling algorithms such as the ADASYN, SMOTE, and Borderline-SMOTE are discussed. Furthermore, the performance of SMOTE is superior to conventional imbalanced data oversampling algorithms, and it can be used to diagnose pathological signals in real-world applications.
引用
收藏
页数:16
相关论文
共 50 条
  • [21] A Hybrid parallel deep learning model for efficient intrusion detection based on metric learning
    Cai, Shaokang
    Han, Dezhi
    Yin, Xinming
    Li, Dun
    Chang, Chin-Chen
    CONNECTION SCIENCE, 2022, 34 (01) : 551 - 577
  • [22] AUC OPTIMIZATION FOR DEEP LEARNING BASED VOICE ACTIVITY DETECTION
    Fan, Zi-Chen
    Bai, Zhongxin
    Zhang, Xiao-Lei
    Rahardja, Susanto
    Chen, Jingdong
    2019 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), 2019, : 6760 - 6764
  • [23] Research on Voice Activity Detection Methods Based on Deep Learning
    Bai, Ke
    Yan, Huaicheng
    Li, Hao
    Tang, Nanxi
    Sun, Jiazheng
    Li, Zhichen
    2024 14TH ASIAN CONTROL CONFERENCE, ASCC 2024, 2024, : 1323 - 1328
  • [24] Hospital mortality prediction in traumatic injuries patients: comparing different SMOTE-based machine learning algorithms
    Roghayyeh Hassanzadeh
    Maryam Farhadian
    Hassan Rafieemehr
    BMC Medical Research Methodology, 23
  • [25] A Modular Deep Learning Architecture for Voice Pathology Classification
    Miliaresi, Ioanna
    Pikrakis, Aggelos
    IEEE ACCESS, 2023, 11 : 80465 - 80478
  • [26] Voice pathology detection by using the deep network architecture
    Ankishan, Haydar
    Inam, Sitki Cagdas
    APPLIED SOFT COMPUTING, 2021, 106
  • [27] An efficient rumor detection model based on deep learning and flower pollination algorithm
    Ahsan, Mohammad
    Sinha, Bam Bahadur
    KNOWLEDGE AND INFORMATION SYSTEMS, 2025, 67 (03) : 2691 - 2719
  • [28] SMOTE-based adaptive coati kepler optimized hybrid deep network for predicting the survival of heart failure patients
    Barfungpa, Sonam Palden
    Samantaray, Leena
    Sarma, Hiren Kumar Deva
    MULTIMEDIA TOOLS AND APPLICATIONS, 2024, 83 (24) : 65497 - 65524
  • [29] An efficient deep learning model for tomato disease detection
    Wang, Xuewei
    Liu, Jun
    PLANT METHODS, 2024, 20 (01)
  • [30] An Efficient Deep Learning Model for Olive Diseases Detection
    Alruwaili, Madallah
    Abd El-Ghany, Sameh
    Alanazi, Saad
    Shehab, Abdulaziz
    INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS, 2019, 10 (08) : 486 - 492