An Ensemble CNN With Bayesian Learning Model for Multiclass Classification of Brain Disease Using Adaptive Refinement Network‐Based Segmentation

Brain problems lead to the loss of physical functions like speech and movement. Thus, early brain tumour diagnosis is fundamental for improving the survival of patients. Existing traditional methods follow deep neural structural design where the selection of relevant characteristics descriptors and classifiers is a main challenge. Therefore, the deep learning-based recognition of various abnormalities in the brain has been suggested. Initially, the required brain image is taken from the public dataset. The image data are then passed to the segmentation process, in which the adaptive refinement network (ARN) performs the segmentation as it is robust to outliers and can manage the intricate structure of tumours. Further, enhance the segmentation process by implementing the fitness-based flamingo search algorithm (FFSA), which optimizes the parameters in the segmentation model by efficiently exploring the search area and converging on the most favourable solutions. The resultant segmented images are sent to an ensemble convolutional neural network (CNN) with Bayesian learning (ECNN-BL) for classification. By combining several systems, ensembles can overcome overfitting issues, which lead to better generalization to new data and improved accuracy and robustness. Here, the ensemble CNN is the combination of the visual geometry group-16 (VGG16), residual neural network (Resnet), and Xception that performs effective classification. The superiority of the developed model is determined by taking a similar analysis with existing approaches. From the findings, the designed system is dependable and efficient in identifying brain diseases using the MRI images.