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<p>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 <i>F</i>1-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.</p>

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

  • 期刊论文

  • 发表日期：

    2026年5月28日

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  • Kuljeet Singh,
  • Deepti Malhotra,
  • Sidi Mohamed Sid’El Moctar

摘要

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.