ReViT-RF: A hybrid ResNet-50-Vision Transformer with a Radiomic Feature Fusion framework for lung tumor classification and TNM-based stage prediction using CT images.

Lung cancer is one of the leading causes of cancer-related mortality worldwide, emphasizing the need for accurate, automated, and clinically interpretable diagnostic systems. Although existing deep learning (DL) models have achieved promising performance in classifying lung nodules as benign or malignant, most approaches are limited to binary classification and provide insufficient support for detailed tumor characterization and TNM staging. Furthermore, conventional convolutional neural networks (CNNs) are effective in extracting local morphological features but often fail to capture long-range spatial dependencies. In contrast, transformer-based models can learn global contextual information but may compromise fine-grained local feature representation. To address these limitations, this study proposes a hybrid ResNet-50-Vision Transformer with Radiomic Feature Fusion (ReViT-RF) framework for lung tumor classification and component-wise TNM stage prediction using thoracic computed tomography (CT) images from the publicly available Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) dataset. The primary clinical objectives of the proposed framework are to: (i) classify pulmonary nodules as benign or malignant, and (ii) predict individual TNM components, including primary tumor extent (T), lymph node involvement (N), and metastatic status (M) for malignant cases. To improve tumor visibility and image quality, CT images are initially preprocessed using Contrast-Limited Adaptive Histogram Equalization (CLAHE) and intensity normalization. The proposed ReViT-RF architecture integrates the complementary advantages of ResNet-50 and Vision Transformer (ViT) for comprehensive feature learning. The CNN branch extracts local morphological, textural, and structural characteristics, whereas the transformer branch captures long-range spatial relationships and global contextual dependencies. In addition, handcrafted radiomic features, including shape, size, texture, and spatial descriptors, are fused with deep features to provide clinically meaningful information for accurate TNM component prediction. The dataset is divided into training, validation, and testing subsets using an 80:10:10 ratio, with data augmentation applied only to the training set to prevent information leakage. Experimental results demonstrate that the proposed framework achieves an accuracy of 97.62% for benign-malignant classification, along with high precision, recall, and F1-score values. Moreover, independent prediction of TNM components provides detailed stage-related information rather than relying on a single aggregated stage label, enabling more precise tumor assessment and clinical decision support. Furthermore, explainability analysis using Gradient-weighted Class Activation Mapping (Grad-CAM) identifies the critical tumor regions contributing to model predictions, improving interpretability and enhancing clinician confidence. Overall, by combining hybrid deep feature extraction, radiomic feature fusion, component-wise TNM stage prediction, and explainable AI analysis, the proposed ReViT-RF framework provides a reliable, comprehensive, and clinically interpretable solution for automated lung tumor analysis.