Red Panda Snake optimization based feature selection and attention-enhanced autoencoder for brain tumor classification

Brain tumor classification plays a vital role in clinical diagnosis and treatment planning, yet existing deep-learning models often suffer from feature redundancy, noise sensitivity, and suboptimal feature selection, leading to limited diagnostic accuracy. To address these gaps, this study proposes a novel hybrid framework integrating Multi-Scale Adaptive Bilateral Filtering, Red Panda Integrated Snake (RPS) optimization, and an Attention-Based Dense Autoencoder Network (At_DAENet). The filtering stage enhances MRI quality by preserving edges while removing multi-scale artifacts. A comprehensive set of statistical features, including mean, entropy, kurtosis, and skewness, and textural features such as GLCM contrast, homogeneity, energy, correlation, and dissimilarity, is extracted and subsequently optimized using the RPS algorithm. The RPS method combines the global search capability of Red Panda foraging with the adaptive exploitation behavior of Snake Optimization (SOA), enabling superior convergence performance and reducing the risk of local-optima stagnation. These optimized features are further refined through At_DAENet, where dense connectivity and attention mechanisms prioritize discriminative tumor-specific patterns. Experimental results demonstrate that the proposed model achieves 0.998 accuracy, 0.991 precision, 1.0 recall, 0.9959 F-score, and 0.988 specificity on the BraTS dataset, and 0.972 accuracy, 0.969 precision, 1.0 recall, 0.968 F-score, and 0.919 specificity on the FigShare dataset, outperforming existing state-of-the-art approaches. These findings highlight the robustness of the proposed RPS-driven attention-enhanced classification pipeline and its potential value for reliable brain tumor diagnosis.