Equilibrium Optimization Algorithm with Deep Learning Based Brain Tumor Segmentation and Classification on Magnetic Resonance Imaging

Brain tumors (BTs) are a serious medical condition that can have significant impacts on individuals. These tumors typically originate in various parts of the brain and can be detected using Magnetic Resonance Imaging (MRI), which has become an essential tool for medical research. However, manual analysis of MRI images for BT segmentation is a time-consuming and error-prone process. To address this challenge, automated methods based on deep learning algorithms have been developed for fast and accurate detection of anomalous brain regions. In this article, we propose a novel approach called Equilibrium Optimizer Algorithm with Deep Learning-based Brain Tumor Segmentation and Classification (EOADL-BTSC) for brain tumor segmentation and classification using MRI images. Our method uses enhancement of contrast and skull stripping to preprocess the images, followed by an attention-inception-based UNet model for segmentation, a capsule network (CapsNet) model for feature extraction, and a cascaded recurrent neural network (CRNN) for classification. To optimize the performance of our proposed method, we use Equilibrium Optimizer Algorithm (EOA) to fine-tune the hyperparameters of the UNet model. We evaluate performance of our approach on a benchmark database and compare it with other recent approaches. experimental results demonstrate that the EOADL-BTSC methodology outperforms the other approaches terms of several performance measures. In summary, the proposed DL-BTSC methodology provides promising solution for automated brain tumor segmentation and classification using MRI images. It has potential to assist medical professionals in accurate and fast detection of brain tumors, leading to better medical analysis and treatment planning. Our proposed method achieves the maximum accu_y, sens_y, spec_y values of 99.15% 98.78%, and 99.15% respectively. They also note that the proposed approach requires fewer parameters and has a quicker segmentation time than previous approaches.