A Global-Local Interaction Modeling Network with Adaptive Feature Optimization for Brain Tumor Classification Using MRI
Received: 20 March 2026 | Revised: 20 April 2026 | Accepted: 1 May 2026 | Online: 20 May 2026
Corresponding author: Subia Salma
Abstract
Reliable classification of brain tumors remains challenging for computer-aided diagnosis, since each tumor type can look very different, only sparse data exist, and images and genomic profiles contain noise. This study presents an efficient deep learning framework that mixes three components: a dimensional transformation, an attention step, and regularized learning. First, a method turns high-dimensional sparse imaging feature intensity variation pattern records into structured 2D RGB images. This step allows ordinary convolutional and attention networks to process the data, while intensity variation patterns that matter biologically stay intact. To reduce noise and sparseness, an Adaptive Feature Optimization Algorithm (AFOA) applies clustering-based imaging feature selection, keeps informative candidates, and drives tumor-aware multi-channel MRI features, raising feature quality and stabilizing training. On the cleaned data, a global-local interaction modeling network uses a Tumor-Aware Feature Refinement Block (TAFRB) and multi-head self-attention to learn local intensity pattern variation details or global context with reduced computation. Label smoothing regularization addresses overfitting when data are scarce and widens gaps between classes. Tests on public brain tumor datasets show that the proposed framework outperforms current CNNs with transformer models in accuracy, precision, recall, and F1-score, and generalizes well across dataset sizes. The results show that the proposed approach offers a robust, scalable, and fast route for brain tumor classification and can support clinical decisions.
Keywords:
brain tumor classification, intensity pattern variation, Adaptive Feature Optimization Algorithm (AFOA), global-local interaction modelling network, multi-head self-attention, label smoothing regularisation, computer-aided diagnosisReferences
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Copyright (c) 2026 Subia Salma, S. C. Lingareddy, S. Shilpashree, Vineet Kumar
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