Lung Disease Classification
11 papers with code • 0 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Lung Disease Classification
Chest wall lung sound dataset
Most implemented papers
ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases
arnoweng/CheXNet
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CVPR 2017
The chest X-ray is one of the most commonly accessible radiological examinations for screening and diagnosis of many lung diseases.
CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison
On a validation set of 200 chest radiographic studies which were manually annotated by 3 board-certified radiologists, we find that different uncertainty approaches are useful for different pathologies.
Boosted Cascaded Convnets for Multilabel Classification of Thoracic Diseases in Chest Radiographs
Chest X-ray is one of the most accessible medical imaging technique for diagnosis of multiple diseases.
Respiratory diseases recognition through respiratory sound with the help of deep neural network
Prediction of respiratory diseases such as COPD(Chronic obstructive pulmonary disease), URTI(upper respiratory tract infection), Bronchiectasis, Pneumonia, Bronchiolitis with the help of deep neural networks or deep learning.
Self Pre-training with Masked Autoencoders for Medical Image Classification and Segmentation
cvlab-stonybrook/SelfMedMAE
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Masked Autoencoder (MAE) has recently been shown to be effective in pre-training Vision Transformers (ViT) for natural image analysis.
Attention-based Saliency Maps Improve Interpretability of Pneumothorax Classification
Conclusion: ViTs performed similarly to CNNs in CXR classification, and their attention-based saliency maps were more useful to radiologists and outperformed GradCAM.
RDLINet: A Novel Lightweight Inception Network for Respiratory Disease Classification Using Lung Sounds
rsarka34/RDLINet
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IEEE Transactions on Instrumentation and Measurement 2023
The main aim of this article is to propose a novel lightweight inception network to classify a wide spectrum of respiratory diseases using lung sound signals.
AsTFSONN: A Unified Framework Based on Time-Frequency Domain Self-Operational Neural Network for Asthmatic Lung Sound Classification
rsarka34/AsTFSONN
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2023 IEEE International Symposium on Medical Measurements and Applications (MeMeA) 2023
Asthma is one of the most severe chronic respiratory diseases which can be diagnosed using several modalities, such as lung function test or spirometric measures, peak flow meter-based measures, sputum eosinophils, pathological speech, and wheezing events of the lung auscultation sound, etc.
AsthmaSCELNet: A Lightweight Supervised Contrastive Embedding Learning Framework For Asthma Classification Using Lung Sounds
rsarka34/AsthmaSCELNet-INTERSPEECH
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Interspeech 2023
The AsthmaSCELNet consists of two stages: embedding learning using a lightweight embedding extraction backbone module that extracts compact embedding from the melspectrogram, and classification by the learnt embeddings using multi-layer perceptrons.
CROCODILE: Causality aids RObustness via COntrastive DIsentangled LEarning
Due to domain shift, deep learning image classifiers perform poorly when applied to a domain different from the training one.
