Synergistic Deep Learning Fusion for Precision Lung Cancer Staging.
Authors
Affiliations (5)
Affiliations (5)
- Department of Biomedical Engineering, Saveetha Engineering College, Chennai, Tamil Nadu, India.
- Department of ECE, Loyola-ICAM College of Engineering and Technology, Chennai, India.
- Department of ECE, Rajalakshmi Institute of Technology, Chennai, India.
- Department of Medical Electronics, Saveetha Engineering College, Chennai, India.
- Department of CSE, Sri Venkateswara College of Engineering,Chennai, India.
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.