Pyramidal attention-based T network for brain tumor classification: a comprehensive analysis of transfer learning approaches for clinically reliable and reliable AI hybrid approaches

Brain tumors are a significant challenge to human health as they impair the proper functioning of the brain and the general quality of life, thus requiring clinical intervention through early and accurate diagnosis. Although current state-of-the-art deep learning methods have achieved remarkable progress, there is still a gap in the representation learning of tumor-specific spatial characteristics and the robustness of the classification model on heterogeneous data. In this paper, we introduce a novel Pyramidal Attention-Based bi-partitioned T Network (PABT-Net) that combines the hierarchical pyramidal attention mechanism and T-block based bi-partitioned feature extraction, and a self-convolutional dilated neural classifier as the final task. Such an architecture increases the discriminability of the space and decreases the false forecasting by adaptively focusing on informative areas in brain MRI images. The model was thoroughly tested on three benchmark datasets, Figshare Brain Tumor Dataset, Sartaj Brain MRI Dataset, and Br35H Brain Tumor Dataset, containing 7023 images labeled in four tumor classes: glioma, meningioma, no tumor, and pituitary tumor. It attained an overall classification accuracy of 99.12%, a mean cross-validation accuracy of 98.77%, a Jaccard similarity index of 0.986, and a Cohen's Kappa value of 0.987, indicating superb generalization and clinical stability. The model's effectiveness is also confirmed by tumor-wise classification accuracies: 96.75%, 98.46%, and 99.57% in glioma, meningioma, and pituitary tumors, respectively. Comparative experiments with the state-of-the-art models, including VGG19, MobileNet, and NASNet, were carried out, and ablation studies proved the effectiveness of NASNet incorporation. To capture more prominent spatial-temporal patterns, we investigated hybrid networks, including NASNet with ANN, CNN, LSTM, and CNN-LSTM variants. The framework implements a strict nine-fold cross-validation procedure. It integrates a broad range of measures in its evaluation, including precision, recall, specificity, F1-score, AUC, confusion matrices, and the ROC analysis, consistent across distributions. In general, the PABT-Net model has high potential to be a clinically deployable, interpretable, state-of-the-art automated brain tumor classification model.