This project is about classifying respiratory sounds using Attention and Vision Transformer on ICBHI dataset..
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Updated
May 12, 2024 - Jupyter Notebook
This project is about classifying respiratory sounds using Attention and Vision Transformer on ICBHI dataset..
RDLINet: A Novel Lightweight Inception Network for Respiratory Disease Classification Using Lung Sounds (IEEE TIM-2024)
AsTFSONN: A Unified Framework Based on Time-Frequency Domain Self-Operational Neural Network for Asthmatic Lung Sound Classification (IEEE MeMeA-2024)
A Novel Multi-Head Self-Organized Operational Neural Network Architecture for Chronic Obstructive Pulmonary Disease Detection Using Lung Sounds (IEEE TASLP-2024)
Official Implementation of Non-Contrastive Self-Supervised Learning with UNO Process for Respiratory Sound Analysis
Automated respiratory sound classification using ML and deep learning on the ICBHI 2017 dataset. Patient-aware evaluation with SVM, CNN, CRNN, and ResNet18 across cycle-level (4-class + binary) and patient-level disease diagnosis.
Pulmo-TS2ONN: A Novel Triple Scale Self Operational Neural Network for Pulmonary Disorder Detection Using Respiratory Sounds (IEEE TIM-2024)
Code accompanying ESANN 2025 submission "Exploring Model Architectures for Real-Time Lung Sound Event Detection". Dataset used was ICBHI 2017.
Respiratory sound classification on ICBHI 2017 — comparing PANNs+BiGRU, EfficientNet-B0, and Audio Spectrogram Transformer (AST) with best ICBHI score of 0.6835
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