ArticleFrontiers in public health2022
Accurate classification of lung nodules on CT images using the TransUnet.
Article in Frontiers in public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed.
- Patients' Perspectives on the Implementation of AI in Radiological Diagnostics: Focus Group Study.Journal of medical Internet research · 2026Article
- An intelligent lung nodule classification model using 3D Trans-DenseUnet++-based lung nodule segmentation.Scientific reports · 2026Article
- The rise of artificial intelligence in respiratory primary care and pulmonology: a scoping review.NPJ primary care respiratory medicine · 2026Article
- Development and validation of a transformer-based deep learning model for predicting distant metastasis in non-small cell lung cancer usingClinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Article
- Integrating central-slice anatomy and MRF-mapped radiomics dynamics for lung adenocarcinoma subtyping.Quantitative imaging in medicine and surgery · 2026Article
- The role of combined ultrasonic BI-RADS and ultrasound elastography in discriminating between benign and malignant breast nodules.Medicine · 2026Article
- Article
- A Novel Automatic Lung Nodule Classification Scheme using Fusion Ghost Convolution and Hybrid Normalization in Chest CTsCurrent medical imaging · 2025Article
- Machine learning and deep learning models for the diagnosis of apical periodontitis: a scoping review.Clinical oral investigations · 2024Article
- Automated measurement and grading of knee cartilage thickness: a deep learning-based approach.Frontiers in medicine · 2024Article
- Improving diagnosis and prognosis of lung cancer using vision transformers: a scoping review.BMC medical imaging · 2023Article
- Redefining Radiology: A Review of Artificial Intelligence Integration in Medical Imaging.Diagnostics (Basel, Switzerland) · 2023Review
- A proposed methodology for detecting the malignant potential of pulmonary nodules in sarcoma using computed tomographic imaging and artificial intelligence-based models.Frontiers in oncology · 2023Article
- Artificial intelligence in respiratory care: Current scenario and future perspective.Annals of thoracic medicineArticle
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3 authors.
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Abstract
Background: Computed tomography (CT) is an effective way to scan for lung cancer. The classification of lung nodules in CT screening is completely doctor dependent, which has drawbacks, including difficulty classifying tiny nodules, subjectivity, and high false-positive rates. In recent years, deep convolutional neural networks, a deep learning technology, have been shown to be effective in medical imaging diagnosis. Herein, we propose a deep convolutional neural network technique (TransUnet) to automatically classify lung nodules accurately. Methods: TransUnet consists of three parts: the transformer, the Unet, and global average pooling (GAP). The transformer encodes discriminative features via global self-attention modeling on CT image patches. The Unet, which collects context by constricting route, enables exact lunge nodule localization. The GAP categorizes CT images, assigning each sample a score. Python was employed to pre-process all CT images in the LIDI-IDRI, and the obtained 8,474 images (3,259 benign and 5,215 lung nodules) were used to evaluate the method's performance. Results: The accuracies of TransUnet in the training and testing sets were 87.90 and 84.62%. The sensitivity, specificity, and AUC of the proposed TransUnet on the testing dataset were 70.92, 93.17, and 0.862%, respectively (0.844-0.879). We also compared TransUnet to three well-known methods, which outperformed these methods. Conclusion: The experimental results on LIDI-IDRI demonstrated that the proposed TransUnet has a great performance in classifying lung nodules and has a great potential application in diagnosing lung cancer.
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