Authors
Sinthia P, Anitha Juliette Albert, Malathi M, Gurumoorthy G, Rajalakshmi S
Published in
Asian Pacific journal of cancer prevention : APJCP. Volume 27. Issue 6. Pages 2335-2343. Jun 01, 2026. Epub Jun 01, 2026.
Abstract
To develop and evaluate an automated deep learning-based lung cancer staging system using computed tomography (CT) scan images.
CT scan images were obtained from publicly available datasets (LIDC-IDRI/TCIA) comprising 1,018 patient scans. The dataset consisted of three subsets, which were: training (70 percent of total), validation (15 percent), and testing (15 percent). Lung region segmentation, anisotropic filtering, and data augmentation were used as preprocessing. To classify lung cancer stages, a customized CNN network based on multi-scale feature extraction and softmax-enabled probabilistic output was trained. Statistical confidence intervals, F1-score, ROC-AUC, recall, accuracy, and precision were used to test the performance of the model.
Using an area under the curve (AUC) of 0.98 (Stage I), 0.96 (Stage II), 0.95 (Stage III) and 0.97 (Stage IV) the proposed model indicates a total classification of 93.0 (95% CI: 91.2-94.8). Statistical analysis revealed a significant improvement compared to baseline CNN models (p < 0.05). Compared with state-of-the-art techniques, quantitative comparisons showed either equivalent performance or slightly higher performance, particularly in separating between early-stage (I-II) and advanced-stage (III-IV) disease.
The findings demonstrate that the suggested CNN-based architecture can effectively and precisely classify the stage of lung cancer based on CT images, which assists in automated clinical decision-making and enhances the early detection process.
PMID:
42345183
Bibliographic data and abstract were imported from PubMed on 25 Jun 2026.
Read full publication at:
Please sign in
to see all details.
Advertisement
Stats
- Recommendations n/a n/a positive of 0 vote(s)
- Views 8
- Comments 0