eng Oriental Scientific Publishing Company Biomedical and Pharmacology Journal 0974-6242 2026-04-21 19 2 71496 article Vimala Mannarsamy 1 Nandhini Mani 1 Jasmine Shahul Hameed 2 Ranjith kumar Paulraj 1 Department of Electronics and Communication, P.S.R. Engineering College, Sivakasi, Tamilnadu, India 3Department of Networking and Communications, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, India Brain tumors arise from the uncontrolled proliferation of abnormal brain cells, with significantly higher mortality rates in both adults and children compared to individuals without such conditions. Accurate and robust classification of brain tumors is essential for timely treatment planning. This study proposes a novel Brain Tumor Classification and Survival Analysis using Multi-Attribute Graph Convolution Network (BTC-SA-MGCN) framework. MRI images from the BRATS 2018 dataset are first denoised using the Generalized Multi-kernel Maximum Correntropy Kalman Filter (GMMCKF), followed by segmentation via Deep Fuzzy Curriculum Clustering (DFCC). Revised Tunable Q-factor Wavelet Transform (RTQWT) is then employed to extract Haralick texture features, which are fed into a Multi-Attribute Graph Convolution Network (MGCN) for benign–malignant classification. Extensive experiments under both 80:20 hold-out and 5-fold cross-validation settings demonstrate that hyperparameter tuning markedly improves performance, achieving up to 98.99% accuracy, 99.00% precision, 98.99% sensitivity, and 99.00% F1-score, with statistically significant gains confirmed by Chi-square tests (p< 0.05 for all cases). Comparative evaluation against 12 recent state-of-the-art methods shows BTC-SA-MGCN attaining the highest accuracy (99.05%) while maintaining balanced precision, sensitivity, and F1-score, outperforming EfficientNet-B0, ResNet-based, and ensemble architectures. These results confirm the effectiveness and robustness of the proposed approach for clinical brain tumor classification tasks. https://biomedpharmajournal.org/vol19no2/brain-tumor-classification-and-survival-analysis-using-multi-attribute-graph-convolution-network/ Classification Deep Fuzzy Curriculum Clustering (DFCC) Generalized Multi-kernel Maximum Brain Tumor Correntropy Kalman Filter (GMMCKF) Multi-Attribute Graph Convolution Network (MAGCN) Revised Tunable Q-factor Wavelet Transform (RTQWT) Survival Prediction