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

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