Abstract
Detecting and segmenting brain tumors from 3D MRI images is a challenging and time-intensive task for clinicians. This research introduces an innovative hybrid architecture for deep learning, comprising a 3D fully convolutional neural network (3D-FCNN), an interval type-2 fuzzy weighting system, and a hybrid transit search optimization-particle swarm optimization (Hybrid-TSO-PSO) algorithm. The proposed models, 3D-FCNN-Hybrid-TSO-PSO and 3D-FCNN-SVM, employ type-2 fuzzy weighting to diminish the quantity of trainable parameters and expedite training on MRI volumetric data. The Hybrid-TSO-PSO optimization approach integrates the heuristic strengths of TSO with the rapid convergence attributes of PSO, enhancing learning stability and augmenting the precision of segmentation and classification. Assessments were conducted on the BraTS 2019, BraTS 2020, and a portion of the BraTS 2021 datasets, comprising 300 3D MRI images (230 high-grade HGG and 70 low-grade LGG glioma specimens). During the testing phase, the 3D-FCNN-Hybrid-TSO-PSO model attained an accuracy of 98.1%, sensitivity of 98.9%, specificity of 95.0%, and a Dice score of 0.987, whereas the 3D-FCNN-SVM model earned an accuracy of 95.2%. This method not only enhances accuracy but also decreases training duration by as much as sixfold relative to traditional architectures, serving as an efficient and precise diagnostic aid for the identification and classification of brain cancers.