A Bee Colony Optimization Framework with Fuzzy Softmax Confidence Modeling for Multiclass Brain Tumor MRI Classification

Brain tumor classification from magnetic resonance imaging (MRI) remains challenging in settings where only image-level labels are available and tumor classes exhibit overlapping visual characteristics. In this study, we consider the publicly available Brain Tumor MRI Dataset from Kaggle, a four-class dataset composed of 2D MRI slices belonging to the categories glioma, meningioma, pituitary tumor, and no tumor. Accordingly, the proposed framework is formulated as a slice-based multiclass classification approach rather than a volumetric 3D analysis pipeline. We propose a lightweight and interpretable framework that integrates handcrafted multiscale MRI descriptors, an artificial neural network (ANN), Bee Colony Optimization (BCO)-based neural architecture search, and fuzzy softmax confidence modeling. Each MRI slice is represented by a compact 9-dimensional feature vector derived from intensity, local entropy, and gradient magnitude computed globally and over non-overlapping spatial blocks. The ANN design problem is formulated as a discrete–continuous optimization task, where BCO is employed to optimize network architecture and training hyperparameters by maximizing validation macro- F 1 . To quantify predictive reliability, the softmax outputs are interpreted as fuzzy class memberships and further analyzed using maximum membership, normalized entropy, decision margin, and ambiguity measures, enabling confidence-aware reliability assessment. These fuzzy confidence descriptors enable confidence-threshold-based selective classification and rejection of low-confidence predictions. Across repeated runs, the optimized BCO-ANN achieved a mean test accuracy of 0.781 ± 0.009 , mean macro- F 1 of 0.775 ± 0.010 , mean Brier score of 0.319 ± 0.012 , and mean Expected Calibration Error (ECE) of 0.0273 ± 0.0080 , compared with 0.748 ± 0.011 , 0.738 ± 0.013 , 0.352 ± 0.010 , and 0.0446 ± 0.0071 for the baseline ANN, respectively. Under confidence-threshold-based rejection, selective macro- F 1 increased to 0.820 ± 0.009 at τ = 0.55 and to 0.874 ± 0.020 at τ = 0.85 , with the expected reduction in coverage. These results indicate that the proposed framework provides a transparent and reproducible approach for optimization-aware and confidence-aware multiclass brain tumor MRI classification in a lightweight handcrafted feature setting.