ArticleFrontiers in physiology2022
CCT: Lightweight compact convolutional transformer for lung disease CT image classification.
Article in Frontiers in physiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed, 7 citations in OpenAlex.
- A Novel AAF-SwinT Model for Automatic Recognition of Abnormal Goat Lung Sounds.Animals : an open access journal from MDPI · 2026Article
- Vision Transformers in Medical Imaging: a Comprehensive Review of Advancements and Applications Across Multiple Diseases.Journal of imaging informatics in medicine · 2025Review
- Convolutional Neural Network-Vision Transformer Architecture with Gated Control Mechanism and Multi-Scale Fusion for Enhanced Pulmonary Disease Classification.Diagnostics (Basel, Switzerland) · 2024Article
- Non-small cell lung cancer detection through knowledge distillation approach with teaching assistant.PloS one · 2024Article
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Authors and funding
3 authors at 1 institution in 1 country.
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Abstract
Computed tomography (CT) imaging results are an important criterion for the diagnosis of lung disease. CT images can clearly show the characteristics of lung lesions. Early and accurate detection of lung diseases helps clinicians to improve patient care effectively. Therefore, in this study, we used a lightweight compact convolutional transformer (CCT) to build a prediction model for lung disease classification using chest CT images. We added a position offset term and changed the attention mechanism of the transformer encoder to an axial attention mechanism module. As a result, the classification performance of the model was improved in terms of height and width. We show that the model effectively classifies COVID-19, community pneumonia, and normal conditions on the CC-CCII dataset. The proposed model outperforms other comparable models in the test set, achieving an accuracy of 98.5% and a sensitivity of 98.6%. The results show that our method achieves a larger field of perception on CT images, which positively affects the classification of CT images. Thus, the method can provide adequate assistance to clinicians.
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