ArticleJournal of personalized medicine2022
LungNet22: A Fine-Tuned Model for Multiclass Classification and Prediction of Lung Disease Using X-ray Images.
Article in Journal of personalized medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 2 of them syntheses that pooled it.
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Who cites it
19 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Diagnostic Performance of Artificial Intelligence-Based Methods for Tuberculosis Detection: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Deep learning for pneumothorax diagnosis: a systematic review and meta-analysis.European respiratory review : an official journal of the European Respiratory Society · 2023Pooled it
- Deep Active Learning for Lung Disease Severity Classification from Chest X-rays: Learning with Less Data in the Presence of Class Imbalance.Journal of imaging informatics in medicine · 2026Article
- Integrating Local and Global Representation Learning for Pediatric Pneumonia Detection: A Hybrid CNN-Transformer Ensemble Framework.Diagnostics (Basel, Switzerland) · 2026Article
- Explainable pulmonary fibrosis detection using edge-strengthened dilated holistic edge detection-based lung segmentation and ResNet-V2 classification.Frontiers in artificial intelligence · 2026Article
- Vision Transformers in Medical Imaging: a Comprehensive Review of Advancements and Applications Across Multiple Diseases.Journal of imaging informatics in medicine · 2025Review
- Evaluating the impact of view position in X-ray imaging for the classification of lung diseases.Physical and engineering sciences in medicine · 2025Article
- Visual and Predictive Assessment of Pneumothorax Recurrence in Adolescents Using Machine Learning on Chest CT.Journal of clinical medicine · 2025Article
- Accurate Liver Fibrosis Detection Through Hybrid MRMR-BiLSTM-CNN Architecture with Histogram Equalization and Optimization.Journal of imaging informatics in medicine · 2024Article
- DeepChestGNN: A Comprehensive Framework for Enhanced Lung Disease Identification through Advanced Graphical Deep Features.Sensors (Basel, Switzerland) · 2024Article
- Lung-DT: An AI-Powered Digital Twin Framework for Thoracic Health Monitoring and Diagnosis.Sensors (Basel, Switzerland) · 2024Article
- Explainable artificial intelligence approaches for COVID-19 prognosis prediction using clinical markers.Scientific reports · 2024Article
- Prediction of Tuberculosis From Lung Tissue Images of Diversity Outbred Mice Using Jump Knowledge Based Cell Graph Neural Network.IEEE access : practical innovations, open solutions · 2024Article
- Enhancing surgical decision-making in NEC with ResNet18: a deep learning approach to predict the need for surgery through x-ray image analysis.Frontiers in pediatrics · 2024Article
- Automated breast tumor ultrasound image segmentation with hybrid UNet and classification using fine-tuned CNN model.Heliyon · 2023Article
- Automated COVID-19 detection with convolutional neural networks.Scientific reports · 2023Article
- OView-AI Supporter for Classifying Pneumonia, Pneumothorax, Tuberculosis, Lung Cancer Chest X-ray Images Using Multi-Stage Superpixels Classification.Diagnostics (Basel, Switzerland) · 2023Article
- Three-Dimensional Postoperative Results Prediction for Orthognathic Surgery through Deep Learning-Based Alignment Network.Journal of personalized medicine · 2022Article
- Deep learning-based analysis of COVID-19 X-ray images: Incorporating clinical significance and assessing misinterpretation.Digital healthReview
Corrections and comments
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Authors and funding
7 authors.
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Abstract
In recent years, lung disease has increased manyfold, causing millions of casualties annually. To combat the crisis, an efficient, reliable, and affordable lung disease diagnosis technique has become indispensable. In this study, a multiclass classification of lung disease from frontal chest X-ray imaging using a fine-tuned CNN model is proposed. The classification is conducted on 10 disease classes of the lungs, namely COVID-19, Effusion, Tuberculosis, Pneumonia, Lung Opacity, Mass, Nodule, Pneumothorax, and Pulmonary Fibrosis, along with the Normal class. The dataset is a collective dataset gathered from multiple sources. After pre-processing and balancing the dataset with eight augmentation techniques, a total of 80,000 X-ray images were fed to the model for classification purposes. Initially, eight pre-trained CNN models, AlexNet, GoogLeNet, InceptionV3, MobileNetV2, VGG16, ResNet 50, DenseNet121, and EfficientNetB7, were employed on the dataset. Among these, the VGG16 achieved the highest accuracy at 92.95%. To further improve the classification accuracy, LungNet22 was constructed upon the primary structure of the VGG16 model. An ablation study was used in the work to determine the different hyper-parameters. Using the Adam Optimizer, the proposed model achieved a commendable accuracy of 98.89%. To verify the performance of the model, several performance matrices, including the ROC curve and the AUC values, were computed as well.
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