ArticleJournal of personalized medicine2023
Multi-Label Classification of Chest X-ray Abnormalities Using Transfer Learning Techniques.
Article in Journal of personalized medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- AMFF-Net: An Adaptive Multi-Layer Feature Fusion Network Based on ConvNeXtV2-B for Medical Image Classification.Bioengineering (Basel, Switzerland) · 2026Article
- CPM-XNet: Annotation-Efficient Deep-Learning Framework for Detecting Tuberculosis in Chest X-Ray Images.Diagnostics (Basel, Switzerland) · 2026Article
- Projection-Related Bias in the Detection of Thoracic Abnormalities: A Large-Scale Analysis of the NIH ChestX-Ray14 Dataset.Journal of imaging · 2026Article
- Evidence-Guided Diagnostic Reasoning for Pediatric Chest Radiology Based on Multimodal Large Language Models.Journal of imaging · 2026Article
- Scalable Unimodal and Multimodal Deep Learning for Multi-Label Chest Disease Detection: A Comparative Analysis.Diagnostics (Basel, Switzerland) · 2026Article
- Hybrid ConvNeXtV2-ViT Architecture with Ontology-Driven Explainability and Out-of-Distribution Awareness for Transparent Chest X-Ray Diagnosis.Diagnostics (Basel, Switzerland) · 2026Article
- Anatomy-Guided Hybrid CNN-ViT Model with Neuro-Symbolic Reasoning for Early Diagnosis of Thoracic Diseases Multilabel.Diagnostics (Basel, Switzerland) · 2026Article
- A monotonic multi-expert Vision Transformer for clinically reliable chest X-ray classification.Frontiers in medicine · 2026Article
- A novel expert-annotated single-cell dataset for thyroid cancer diagnosis with deep learning benchmarks.PLOS digital health · 2025Article
- CXR-MultiTaskNet a unified deep learning framework for joint disease localization and classification in chest radiographs.Scientific reports · 2025Article
- IHRAS: Automated Medical Report Generation from Chest X-Rays via Classification, Segmentation, and LLMs.Bioengineering (Basel, Switzerland) · 2025Article
- RADAI: A Deep Learning-Based Classification of Lung Abnormalities in Chest X-Rays.Diagnostics (Basel, Switzerland) · 2025Article
- Enhancing Multi-Label Chest X-Ray Classification Using an Improved Ranking Loss.Bioengineering (Basel, Switzerland) · 2025Article
- Automatic measurement of X-ray radiographic parameters based on cascaded HRNet model from the supraspinatus outlet radiographs.Quantitative imaging in medicine and surgery · 2025Article
- Autonomous chest x-ray image classification, capabilities and prospects: rapid evidence assessment.Frontiers in digital health · 2025Review
- Artificial Intelligence in Medical Metaverse: Applications, Challenges, and Future Prospects.Current medical science · 2024Review
- The two-stage detection-after-segmentation model improves the accuracy of identifying subdiaphragmatic lesions.Scientific reports · 2024Article
- Independent evaluation of the accuracy of 5 artificial intelligence software for detecting lung nodules on chest X-rays.Quantitative imaging in medicine and surgery · 2024Article
- Clinical Trial Validation of Automated Segmentation and Scoring of Pulmonary Cysts in Thoracic CT Scans.Diagnostics (Basel, Switzerland) · 2024Article
- DeepChestGNN: A Comprehensive Framework for Enhanced Lung Disease Identification through Advanced Graphical Deep Features.Sensors (Basel, Switzerland) · 2024Article
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Authors and funding
16 authors.
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Abstract
In recent years, deep neural networks have enabled countless innovations in the field of image classification. Encouraged by success in this field, researchers worldwide have demonstrated how to use Convolutional Neural Network techniques in medical imaging problems. In this article, the results were obtained through the use of the EfficientNet in the task of classifying 14 different diseases based on chest X-ray images coming from the NIH (National Institutes of Health) ChestX-ray14 dataset. The approach addresses dataset imbalances by introducing a custom split to ensure fair representation. Binary cross entropy loss is utilized to handle the multi-label difficulty. The model architecture comprises an EfficientNet backbone for feature extraction, succeeded by sequential layers including GlobalAveragePooling, Dense, and BatchNormalization. The main contribution of this paper is a proposed solution that outperforms previous state-of-the-art deep learning models average area under the receiver operating characteristic curve-AUC-ROC (score: 84.28%). The usage of the transfer-learning technique and traditional deep learning engineering techniques was shown to enable us to obtain such results on consumer-class GPUs (graphics processing units).
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