ArticleCancers2025
Transfer Learning-Based Integration of Dual Imaging Modalities for Enhanced Classification Accuracy in Confocal Laser Endomicroscopy of Lung Cancer.
Article in Cancers, 2025. 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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Who cites it
4 citing papers in PubMed.
- Application of confocal laser endomicroscopy in the diagnosis of peripheral pulmonary lesions: a narrative review.Journal of thoracic disease · 2026Review
- Probe-based confocal laser endomicroscopy imaging features of malignant lung nodules: results from a prospectiveJournal of thoracic disease · 2025Article
- An enhanced fusion of transfer learning models with optimization based clinical diagnosis of lung and colon cancer using biomedical imaging.Scientific reports · 2025Article
- Ex vivo use of probe-based confocal laser endomicroscopy in lung cancer visualization: an ex vivo pilot study.Romanian journal of morphology and embryology = Revue roumaine de morphologie et embryologieArticle
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7 authors.
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Abstract
BACKGROUND/
objectivesLung cancer remains the leading cause of cancer-related mortality, underscoring the need for improved diagnostic methods. This study seeks to enhance the classification accuracy of confocal laser endomicroscopy (pCLE) images for lung cancer by applying a dual transfer learning (TL) approach that incorporates histological imaging data.
methodsHistological samples and pCLE images, collected from 40 patients undergoing curative lung cancer surgeries, were selected to create 2 balanced datasets (800 benign and 800 malignant images each). Three CNN architectures-AlexNet, GoogLeNet, and ResNet-were pre-trained on ImageNet and re-trained on pCLE images (confocal TL) or using dual TL (first re-trained on histological images, then pCLE). Model performance was evaluated using accuracy and AUC across 50 independent runs with 10-fold cross-validation.
resultsThe dual TL approach statistically significant outperformed confocal TL, with AlexNet achieving a mean accuracy of 94.97% and an AUC of 0.98, surpassing GoogLeNet (91.43% accuracy, 0.97 AUC) and ResNet (89.87% accuracy, 0.96 AUC). All networks demonstrated statistically significant (
conclusionsDual TL, integrating histological and pCLE imaging, results in a statistically significant improvement in lung cancer classification. This approach offers a promising framework for enhanced tissue classification. and with future development and testing, iy has the potential to improve patient outcomes.
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