Abstract / Summary
Accurate lung tumor segmentation and classification from computed tomography (CT) images remain challenging because of heterogeneous tumor appearance, indistinct boundaries, and inter-class similarity. This study presents BAMR-HF, an integrated framework combining boundary-aware segmentation, hybrid radiomic–deep feature fusion, and context-aware classification. BAMR-Net performs tumor segmentation using multi-scale residual learning and boundary refinement, while TRFF-Net combines radiomic and deep features through learnable weighted fusion, while MTCA-Net incorporates tumor and peritumoral contextual information. The framework was evaluated on the processed Lung Cancer Segmentation Dataset with Lung-RADS Class comprising 972 CT images, with 708 images in the training partition and 264 images in the repository-defined held-out test partition. The source labels were consolidated into Mild (LR2), Moderate (LR3), and Severe (LR4A–LR4B) categories. BAMR-Net achieved a test Dice score of 0.8563. TRFF-Net and MTCA-Net achieved test accuracies of 0.9038 and 0.9327, respectively, while the complete BAMR-HF framework attained an accuracy of 0.9478, F1-score of 0.9476, AUC of 0.9872, and MCC of 0.9215. These findings support the complementary use of boundary-aware segmentation, hybrid feature representation, and contextual modeling, while broader validation on independent multi-institutional data remains necessary.