SOTAVerified

Lung Sound Classification

ICBHI-2017 dataset based lung sound classification

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1–15 of 15 papers

TitleStatusHype
Stethoscope-guided Supervised Contrastive Learning for Cross-domain Adaptation on Respiratory Sound ClassificationCode1
RDLINet: A Novel Lightweight Inception Network for Respiratory Disease Classification Using Lung SoundsCode1
Respiratory diseases recognition through respiratory sound with the help of deep neural networkCode1
Tri-MTL: A Triple Multitask Learning Approach for Respiratory Disease Diagnosis—0
Crackle Detection In Lung Sounds Using Transfer Learning And Multi-Input Convolitional Neural Networks—0
LungRN+NL: An Improved Adventitious Lung Sound Classification Using Non-Local Block ResNet Neural Network with Mixup Data Augmentation—0
Lung Sound Classification Using Co-tuning and Stochastic Normalization—0
Patient Domain Supervised Contrastive Learning for Lung Sound Classification Using Mobile Phone—0
AsTFSONN: A Unified Framework Based on Time-Frequency Domain Self-Operational Neural Network for Asthmatic Lung Sound ClassificationCode0
Patient-Aware Feature Alignment for Robust Lung Sound Classification:Cohesion-Separation and Global Alignment LossesCode0
A Lightweight CNN Model for Detecting Respiratory Diseases from Lung Auscultation Sounds using EMD-CWT-based Hybrid ScalogramCode0
ILDNet: A Novel Deep Learning Framework for Interstitial Lung Disease Identification Using Respiratory SoundsCode0
LungAttn: advanced lung sound classification using attention mechanism with dual TQWT and triple STFT spectrogramCode0
LungBRN: A Smart Digital Stethoscope for Detecting Respiratory Disease Using bi-ResNet Deep Learning AlgorithmCode0
AsthmaSCELNet: A Lightweight Supervised Contrastive Embedding Learning Framework For Asthma Classification Using Lung SoundsCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1RDLINetAccurcay 99.6—Unverified
2Lightweight CNNAccurcay 98.7—Unverified
3GRUAccurcay 95.7—Unverified