An enhanced machine learning-based prognostic prediction model for patients with AECOPD on invasive mechanical ventilation DOI Creative Commons

Yujie Fu,

Yining Liu,

Chuyue Zhong

и другие.

iScience, Год журнала: 2024, Номер 27(12), С. 111230 - 111230

Опубликована: Окт. 23, 2024

Язык: Английский

Performance evaluation of lung sounds classification using deep learning under variable parameters DOI Creative Commons
Zhaoping Wang, Zhiqiang Sun

EURASIP Journal on Advances in Signal Processing, Год журнала: 2024, Номер 2024(1)

Опубликована: Апрель 15, 2024

Abstract It is desired to apply deep learning models (DLMs) assist physicians in distinguishing abnormal/normal lung sounds as quickly possible. The performance of DLMs depends on feature-related and model-related parameters heavily. In this paper, the relationship between a DLM, i.e., convolutional neural network (CNN) analyzed through experiments. ICBHI 2017 selected dataset. sensitivity analysis classification DLM three parameters, length frame, overlap percentage (OP) successive frames feature type, performed. An augmented balanced dataset acquired by way white noise addition, time stretching pitch shifting. spectrogram mel frequency cepstrum coefficients are used features CNN, respectively. results training test show that there exists significant difference among various parameter combinations. OP sensitive. higher OP, better performance. concluded for fixed sampling 8 kHz, frame size 128, 75% optimum under which relatively no extra computation or storage resources required.

Язык: Английский

Процитировано

0
Detection of Respiratory Diseases from Auscultated Sounds Using VGG16 with Data Augmentation DOI

Intan Kamilia Binti Roslan,

Fumiaki Ehara

Опубликована: Янв. 12, 2024

Lung disease, ranking third globally for causes of death with over 3 million annual fatalities according to the 2015 World Health Organization (WHO), underscores significance lung sound features in respiratory illness diagnosis. Auscultation, a subjective early detection method, led development Computer-Aided Diagnosis (CAD) system employing VGG16 model medical imaging complexity. Data limitations necessitated augmentation, enhancing VGG16's performance compared non-augment results. Respiratory Disease Detection (RDD) task was introduced. Short-Time Fourier Transform (STFT) facilitated audio feature extraction, while VGG16, using transfer learning and fine-tuning, proved effective on Kaggle-sourced dataset. Augmentation techniques, including pitch shifting, time stretching, horizontal flipping, addressed class imbalance. The study introduces innovative data augmentation techniques overcome challenge limited training data, demonstrating effectiveness model's performance.

Язык: Английский

Процитировано

0
Lightweight VGG Rolling Sound Classification Model Based on Combinatorial Features DOI
Rui Shi, Fei Zhang, Yanjiao Li

и другие.

2022 34th Chinese Control and Decision Conference (CCDC), Год журнала: 2024, Номер unknown, С. 2473 - 2478

Опубликована: Май 25, 2024

Язык: Английский

Процитировано

0
Utilizing Support Vector Machines for Signal Processing in Telecommunications DOI
Awakash Mishra,

Deepak Mehta,

Rakesh Arya

и другие.

Lecture notes in electrical engineering, Год журнала: 2024, Номер unknown, С. 287 - 292

Опубликована: Окт. 19, 2024

Язык: Английский

Процитировано

0
An enhanced machine learning-based prognostic prediction model for patients with AECOPD on invasive mechanical ventilation DOI Creative Commons

Yujie Fu,

Yining Liu,

Chuyue Zhong

и другие.

iScience, Год журнала: 2024, Номер 27(12), С. 111230 - 111230

Опубликована: Окт. 23, 2024

Язык: Английский

Процитировано

0