Lung Disease Classification

11 papers with code • 0 benchmarks • 1 datasets

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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 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

stanfordmlgroup/chexpert-labeler 21 Jan 2019

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

Azure/AzureChestXRay 23 Nov 2017

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

victor369basu/Respiratory-diseases-recognition-through-respiratory-sound-with-the-help-of-deep-neural-network 30 Apr 2020

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 10 Mar 2022

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

publishing/attention-based-saliency-map 3 Mar 2023

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 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 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 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

gianlucarloni/crocodile 9 Aug 2024

Due to domain shift, deep learning image classifiers perform poorly when applied to a domain different from the training one.