In this paper, we consider two different approaches of using deep neural networks for cough detection. The cough detection task is cast as a visual recognition problem and as a sequence-to-sequence labeling problem. A convolutional neural network and a recurrent neural network are implemented to address these problems, respectively. We evaluate the performance of the two networks and compare them to other conventional approaches for identifying cough sounds. In addition, we also explore the effect of the network size parameters and the impact of long-term signal dependencies in cough classifier performance. Experimental results show both network architectures outperform traditional methods. Between the two, our convolutional network yields a higher specificity 92.7% whereas the recurrent attains a higher sensitivity of 87.7%.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1109/TBCAS.2016.2598794 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
In Vivo Confocal Microscopy for Automated Detection of Meibomian Gland Dysfunction: A Study Based on Deep Convolutional Neural Networks.
J Imaging Inform Med
January 2025
Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, National Clinical Research Center for Eye Disease, Shanghai, 200080, China.
The objectives of this study are to construct a deep convolutional neural network (DCNN) model to diagnose and classify meibomian gland dysfunction (MGD) based on the in vivo confocal microscope (IVCM) images and to evaluate the performance of the DCNN model and its auxiliary significance for clinical diagnosis and treatment. We extracted 6643 IVCM images from the three hospitals' IVCM database as the training set for the DCNN model and 1661 IVCM images from the other two hospitals' IVCM database as the test set to examine the performance of the model. Construction of the DCNN model was performed using DenseNet-169.
View Article and Find Full Text PDFMulti-class Classification of Retinal Eye Diseases from Ophthalmoscopy Images Using Transfer Learning-Based Vision Transformers.
J Imaging Inform Med
January 2025
College of Engineering, Department of Computer Engineering, Koç University, Rumelifeneri Yolu, 34450, Sarıyer, Istanbul, Turkey.
This study explores a transfer learning approach with vision transformers (ViTs) and convolutional neural networks (CNNs) for classifying retinal diseases, specifically diabetic retinopathy, glaucoma, and cataracts, from ophthalmoscopy images. Using a balanced subset of 4217 images and ophthalmology-specific pretrained ViT backbones, this method demonstrates significant improvements in classification accuracy, offering potential for broader applications in medical imaging. Glaucoma, diabetic retinopathy, and cataracts are common eye diseases that can cause vision loss if not treated.
View Article and Find Full Text PDFAssessment model of ozone pollution based on SHAP-IPSO-CNN and its application.
Sci Rep
January 2025
Shenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang, 110168, Liaoning, China.
The problem of ground-level ozone (O) pollution has become a global environmental challenge with far-reaching impacts on public health and ecosystems. Effective control of ozone pollution still faces complex challenges from factors such as complex precursor interactions, variable meteorological conditions and atmospheric chemical processes. To address this problem, a convolutional neural network (CNN) model combining the improved particle swarm optimization (IPSO) algorithm and SHAP analysis, called SHAP-IPSO-CNN, is developed in this study, aiming to reveal the key factors affecting ground-level ozone pollution and their interaction mechanisms.
View Article and Find Full Text PDFPCMMD: A Novel Dataset of Plasma Cells to Support the Diagnosis of Multiple Myeloma.
Sci Data
January 2025
Federal University of Bahia, Institute of Computing, Salvador, 40170-110, Brazil.
Multiple Myeloma (MM) is a cytogenetically heterogeneous clonal plasma cell proliferative disease whose diagnosis is supported by analyses on histological slides of bone marrow aspirate. In summary, experts use a labor-intensive methodology to compute the ratio between plasma cells and non-plasma cells. Therefore, the key aspect of the methodology is identifying these cells, which relies on the experts' attention and experience.
View Article and Find Full Text PDFBrain State Forecasting for Precise Brain Stimulation: Current Approaches and Future Perspectives.
Neuroimage
January 2025
Center for Mind/Brain Sciences (CIMeC), University of Trento, 38068, Rovereto, (TN), Italy.
Transcranial magnetic stimulation (TMS) has the potential to yield insights into cortical functions and improve the treatment of neurological and psychiatric conditions. However, its reliability is hindered by a low reproducibility of results. Among other factors, such low reproducibility is due to structural and functional variability between individual brains.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!