Inception-based network and multi-spectrogram ensemble applied to predict respiratory anomalies and lung diseases
This paper presents an inception-based deep neural network for detecting lung diseases
using respiratory sound input. Recordings of respiratory sound collected from patients are …
using respiratory sound input. Recordings of respiratory sound collected from patients are …
Inception-Based Network and Multi-Spectrogram Ensemble Applied For Predicting Respiratory Anomalies and Lung Diseases
This paper presents an inception-based deep neural network for detecting lung diseases
using respiratory sound input. Recordings of respiratory sound collected from patients are …
using respiratory sound input. Recordings of respiratory sound collected from patients are …
Robust deep learning framework for predicting respiratory anomalies and diseases
This paper presents a robust deep learning framework developed to detect respiratory
diseases from recordings of respiratory sounds. The complete detection process firstly …
diseases from recordings of respiratory sounds. The complete detection process firstly …
Respirenet: A deep neural network for accurately detecting abnormal lung sounds in limited data setting
Auscultation of respiratory sounds is the primary tool for screening and diagnosing lung
diseases. Automated analysis, coupled with digital stethoscopes, can play a crucial role in …
diseases. Automated analysis, coupled with digital stethoscopes, can play a crucial role in …
CNN-MoE based framework for classification of respiratory anomalies and lung disease detection
This paper presents and explores a robust deep learning framework for auscultation
analysis. This aims to classify anomalies in respiratory cycles and detect diseases, from …
analysis. This aims to classify anomalies in respiratory cycles and detect diseases, from …
RDLINet: A novel lightweight inception network for respiratory disease classification using lung sounds
Respiratory diseases are the world's third leading cause of mortality. Early detection is
critical in dealing with respiratory diseases, as it improves the effectiveness of intervention …
critical in dealing with respiratory diseases, as it improves the effectiveness of intervention …
Deep recurrent neural networks with attention mechanisms for respiratory anomaly classification
In recent years, a variety of deep learning techniques and methods have been adopted to
provide AI solutions to issues within the medical field, with one specific area being audio …
provide AI solutions to issues within the medical field, with one specific area being audio …
Lung sound classification with multi-feature integration utilizing lightweight cnn model
Detecting respiratory diseases is of utmost importance, considering that respiratory ailments
represent one of the most prevalent categories of diseases globally. The initial stage of lung …
represent one of the most prevalent categories of diseases globally. The initial stage of lung …
Transfer Learning Based Diagnosis and Analysis of Lung Sound Aberrations
H Gulzar, J Li, A Manzoor, S Rehmat, U Amjad… - arXiv preprint arXiv …, 2023 - arxiv.org
With the development of computer-systems that can collect and analyze enormous volumes
of data, the medical profession is establishing several non-invasive tools. This work attempts …
of data, the medical profession is establishing several non-invasive tools. This work attempts …
Lung sound classification using snapshot ensemble of convolutional neural networks
T Nguyen, F Pernkopf - … Conference of the IEEE Engineering in …, 2020 - ieeexplore.ieee.org
We propose a robust and efficient lung sound classification system using a snapshot
ensemble of convolutional neural networks (CNNs). A robust CNN architecture is used to …
ensemble of convolutional neural networks (CNNs). A robust CNN architecture is used to …