DeepBrainTumorNet: An effective framework of heuristic-aided brain Tumour detection and classification system using residual Attention-Multiscale Dilated inception network

Abstract

A tumor is formed by controlled and rapid cell growth in the brain. If it is not treated in the early stages, it leads to death. Despite many important efforts and promising solutions, accurate segmentation and classification remain a challenge. Traditional automated models have complex architectures, high computing systems, and large amounts of data. Also, most of the existing models still rely on manual intervention. To address all the limitations, a new deep-learning approach is proposed. Initially, brain images are collected from standard sources and passed to the pre-processing stage. In this stage, the input images are pre-processed using scaling, contrast enhancement, and Anisotropic Diffusion Filtering (ADF). Later, the resultant images are provided to the image segmentation stage. Here, image segmentation is performed using adaptive transunet3+, and also their parameters are optimized by using the Hybrid Beluga Whale Glowworm Swarm Optimization (HBWGSO). Further, brain tumor classifications are performed with the hybridization of Residual Attention Network (RAN) and Multiscale Dilated Inception Network (MDIN) termed RA-MDIN, and the model parameters are optimally selected by using the designed HBWGSO approach. Through the experimentation results, the proposed model tends to provide effective classification results. Thus, the recommended system guarantees to yield relatively satisfactory outcomes over conventional mechanisms.