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

Abstract

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 HIGHLIGHTS• Proposed an EOADL-BTSC technique for Brain Tumor Segmentation and Classification.• Perform data preprocessing in two stages namely CLAHE based contrast enhancement and skull stripping.• Developed EOA with Attention inception based UNet technique is developed for medical image segmentation.• Employ CapsNet based feature extraction and CRNN model for brain disorder classification.