ArticleDiagnostics (Basel, Switzerland)2022
Deep Learning in Multi-Class Lung Diseases' Classification on Chest X-ray Images.
Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
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27 citing papers in PubMed, 1 synthesis or guideline pooled it, 121 citations in OpenAlex.
- Deep learning for pneumothorax diagnosis: a systematic review and meta-analysis.European respiratory review : an official journal of the European Respiratory Society · 2023Pooled it
- A radiology-aware fuzzy deep learning framework with entropy-guided feature selection for robust multi-disease chest X-ray classification across multiple magnifications.Radiological physics and technology · 2026Article
- Classification of pulmonary disorders using pulmonary network with explainable AI framework from chest multi-modal images.Scientific reports · 2026Article
- Benchmarking MedViT and hybrid CNN-ViT architectures for multi-label thoracic disease classification.Scientific reports · 2026Article
- An optimized EfficientNetB0 framework with CLAHE-based preprocessing for accurate multi-class chest X-ray classification.Scientific reports · 2026Article
- LXNet: A lightweight CNN for lung disease classification from Chest X-ray with XAI-based interpretability.PloS one · 2026Article
- Evaluation of large language models in a pulmonology outpatient clinic using structured clinical data and chest radiographs: a single-center prospective observational study.Frontiers in medicine · 2026Article
- Using AI system to detect active tuberculosis in a high-prevalence setting on CT scans: a multi-center study.Scientific reports · 2025Article
- Assessment of a Grad-CAM interpretable deep learning model for HAPE diagnosis: performance and pitfalls in severity stratification from chest radiographs.BMC medical informatics and decision making · 2025Article
- A computer-aided diagnosis (CAD) system based on convolutional neural networks for lung cancer diagnosis from 2D [Journal of applied clinical medical physics · 2025Article
- CXR-MultiTaskNet a unified deep learning framework for joint disease localization and classification in chest radiographs.Scientific reports · 2025Article
- Active and Inactive Tuberculosis Classification Using Convolutional Neural Networks with MLP-Mixer.Bioengineering (Basel, Switzerland) · 2025Article
- An anatomically enhanced and clinically validated framework for lung abnormality classification using deep features and KL divergence.MethodsX · 2025Article
- Two-Stream Convolutional Neural Networks for Breathing Pattern Classification: Real-Time Monitoring of Respiratory Disease Patients.Bioengineering (Basel, Switzerland) · 2024Article
- Prostate Cancer Diagnosis via Visual Representation of Tabular Data and Deep Transfer Learning.Bioengineering (Basel, Switzerland) · 2024Article
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- From Pixels to Pathology: Employing Computer Vision to Decode Chest Diseases in Medical Images.Cureus · 2023Review
- Modification of a Conventional Deep Learning Model to Classify Simulated Breathing Patterns: A Step toward Real-Time Monitoring of Patients with Respiratory Infectious Diseases.Sensors (Basel, Switzerland) · 2023Article
- OView-AI Supporter for Classifying Pneumonia, Pneumothorax, Tuberculosis, Lung Cancer Chest X-ray Images Using Multi-Stage Superpixels Classification.Diagnostics (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
Abstract
Chest X-ray radiographic (CXR) imagery enables earlier and easier lung disease diagnosis. Therefore, in this paper, we propose a deep learning method using a transfer learning technique to classify lung diseases on CXR images to improve the efficiency and accuracy of computer-aided diagnostic systems' (CADs') diagnostic performance. Our proposed method is a one-step, end-to-end learning, which means that raw CXR images are directly inputted into a deep learning model (EfficientNet v2-M) to extract their meaningful features in identifying disease categories. We experimented using our proposed method on three classes of normal, pneumonia, and pneumothorax of the U.S. National Institutes of Health (NIH) data set, and achieved validation performances of loss = 0.6933, accuracy = 82.15%, sensitivity = 81.40%, and specificity = 91.65%. We also experimented on the Cheonan Soonchunhyang University Hospital (SCH) data set on four classes of normal, pneumonia, pneumothorax, and tuberculosis, and achieved validation performances of loss = 0.7658, accuracy = 82.20%, sensitivity = 81.40%, and specificity = 94.48%; testing accuracy of normal, pneumonia, pneumothorax, and tuberculosis classes was 63.60%, 82.30%, 82.80%, and 89.90%, respectively.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.