The area of the brain affected by a brain tumour can be identified using the tumour’s shape, size, location, and border. This study seeks to develop a novel system of classification for brain tumours through pre-processing, segmentation, feature extraction, and tumour classification. An improved median filter will be applied to the input image in this initial phase to improve it. In this step, the image is segmented using a U-net model. Then, characteristics based on the Median Binary Pattern (MBP), the loop, the modified Local Gabor Directional Pattern (LGDiP), and the tumour size are retrieved. A hybrid model that fuses DBN and Bi-LSTM is presented to classify cancers. The optimal weights for both classifiers will be tuned during training to improve the classification performance. For this, BMEBEO (Blue Monkey Extended Bald Eagle Optimization) is proposed, which is a hybrid optimization technique. The suggested model obtains the maximum F-measure of 96.16%%.