ArticleJournal of applied clinical medical physics2025
A computer-aided diagnosis (CAD) system based on convolutional neural networks for lung cancer diagnosis from 2D [
Article in Journal of applied clinical medical physics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- An [18F]-FDG PET/CT Image Dataset for Artificial Intelligence-Based Classification of Lung Cancer.Journal of biomedical physics & engineering · 2026Article
- Article
- A computer-aided diagnosis (CAD) system based on convolutional neural networks for lung cancer diagnosis from 2D [Journal of applied clinical medical physics · 2025Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThis study aims to automatically classify lung conditions into normal, non-small cell lung cancer (NSCLC), and small cell lung cancer (SCLC) using [
methodsPET/CT scans from 146 patients (1974 scans) were retrospectively analyzed using two strategies: (1) transfer learning with pre-trained CNNs, and (2) a custom CNN (Res-SE Net) incorporating residual and squeeze-and-excitation (SE) modules. A patient-based data splitting approach was used to avoid data leakage. Models were trained and validated at the scan level and evaluated at the patient level using majority voting. Grad-CAM was employed to generate lesion-localization heatmaps.
resultsAmong the seven evaluated CNN models, the proposed Res-SE Net demonstrated superior performance, achieving an accuracy of 91.67% and a sensitivity of 92.00% in detecting NSCLC, and an accuracy of 90.14% with a sensitivity of 90.00% for distinguishing SCLC cases. When tested on an external dataset, the model attained an accuracy of 98.00% in binary classification (Normal vs. Cancer). In the three-class classification task, the model achieved an accuracy of 73.02% for NSCLC and 66.26% for SCLC.
conclusionThese findings demonstrate the potential of Res-SE Net architecture for accurate multi-class lung cancer classification using [18F] FDG PET/CT images.
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