Distinguishing between early-stage brain tumors (EBTs) and progressive-stage brain tumors (PBTs) from magnetic resonance imaging (MRI) scans is pivotal, as interpretation complexity challenges neuro-oncologists and impacts timely treatment decisions. Existing deep learning (DL)-based approaches typically handle brain tumor classification and segmentation separately, emphasizing multi-level feature extraction, but neglecting the benefits of integrated feature fusion. This study introduces a novel DL-based clinical pipeline, enhanced with generative artificial intelligence (GenAI), that explicitly fuses features from classification and segmentation models for stage-specific tumor analysis. Our fusion-based framework first classifies brain tumors into EBTs and PBTs using a dedicated classification network (C-Net), incorporating an enhanced-pooled attention block and a dilated fusion block to selectively extract and fuse multi-level features, balancing computational efficiency with feature relevance. Subsequently, stage-specific segmentation network 1 (S1-Net) and segmentation network 2 (S2-Net) leverage hierarchical feature fusion through progressive upsampling, capturing distinct tumor characteristics at multiple abstraction levels. Finally, a clinician-validated GenAI module provides post-segmentation semantic interpretation by analyzing segmentation masks and patient metadata to describe tumor morphology and explicitly report limitations. Aligned with information fusion principles, this integration combines the precision of task-specific models (S1-Net, S2-Net) with expert-guided GenAI reasoning, enhancing interpretability while preserving clinical safety. The effectiveness of the proposed pipeline is validated with open dataset of brain tumor progression: 1) C-Net achieves an accuracy of 85.06%, precision of 86.73%, recall of 85%, and harmonic mean of recall and precision (F1-score) of 85.84%; 2) S1-Net attains a Dice score (DS) of 79.96% and intersection over union (IoU) of 71.22%; and 3) S2-Net achieves 81.14% DS and 72.44% IoU, significantly outperforming state-of-the-art methods.
Sultan et al. (Sun,) studied this question.