VMAM-NET: A Model Agnostic Meta-Learning Network for Rare De Novo Glioblastoma Diagnosis

The diagnosis of grade IV brain tumors, such as de novo glioblastoma, has recently attracted a lot of scientific interest in neuroimaging and deep learning. Glioblastoma, a very rare and highly aggressive brain tumor, poses considerable diagnostic challenges due to restricted data availability and substantial intratumoral heterogeneity. This paper introduces VMAM-NET, a hybrid deep meta-learning model that combines VGG-16-based feature extraction with model-agnostic meta-learning (MAML) to enhance glioblastoma diagnosis in data-scarce settings. The VGG model, initially trained on an Astrocytoma dataset, acquires domain-specific imaging characteristics that the MAML framework utilizes for rapid adaptation to few-shot learning tasks involving glioblastoma samples. The model is evaluated on four reliable MRI datasets, using comprehensive preprocessing and stringent optimization. Experimental findings indicate that VMAM-NET attains training and testing accuracies of 98.69% and 96.71%, respectively, with an F1-score of 0.9694, surpassing traditional deep learning and meta-learning models. The approach offers significant interpretability using gradient-based class activation maps (Grad-CAM), emphasizing tumor-relevant areas in MRI scans. The proposed framework provides a scalable and clinically feasible diagnostic measure, with potential relevance to further rare disorders. VMAM-NET enhances the application of data-efficient artificial intelligence in healthcare under resource-constrained environments.