A brain tumorBrain tumor (BT), which may be benign or malignant, is an abnormal expansion of cells within the brain or central spinal canal. Because these tumors are close to important neurological regions, they may seriously harm an individual's health and impair normal brain function. Timely diagnosis and efficient treatment planning depend on the accurate classification of brain tumorsBrain tumor using medical imaging data. This work presents a thorough method that uses cutting-edge methodologies to get better results in the classification of brain tumorsBrain tumor. This work is based on the BraTS 2020 dataset, which offers a complex and varied collection of brain imaging data. An Adaptive Weighted Frost FilterAdaptive weighted frost filter (AWFF) is used as a preprocessingPreprocessing step to successfully remove quantum noise, improving the quality of the input images. Then, features are extracted using the Limited Receptive Field MechanismLimited receptive field mechanism (LRFM), which makes it possible to capture complex patterns that are essential for tumor categorization. By including a Multi-Scale Residual Network (MSRNet)Multi-Scale Residual network model, the classification procedure is improved by leveraging the skip and depth connections inherent in residual networks. Using the European Night Crawler OptimizationEuropean night crawler optimization (ENCO) technique, the classification model's hyperparameters are optimized. This metaheuristic approach tunes important parameters in a methodical manner, improving the model's presentation and generalization. The experimental findings demonstrate the effectiveness of the suggested technique with a remarkable 99.8% classification accuracy. The suggested method is superior to existing models, as shown by the results of a comparison, underscoring its promise as a reliable solution for brain tumorBrain tumor classification problems.