Benign and Malignant Brain Tumor Segmentation Using a Melody-Search Optimization Algorithm with an Extreme Softplus Learning

Abstract

Brain tumor is a terrible disease that affects people worldwide. The main reason behind the growth of brain tumors is the uncontrolled and abnormal development of cells in the brain. Early detection of such growth improves survival. Therefore, developing automated systems that detect abnormal growth will help radiologists make accurate diagnoses. This paper presents a new metaheuristic-based methodology for early detection of brain tumors using the Melody Search Optimization Algorithm with Extreme Softplus learning. In the pretreatment step, image quality is improved by intensity normalization in combination with an adaptive bilateral filter. Histogram Oriented Gradient (HOG) extracts slope features. Automatic brain tumor segmentation splits tumor into sub-regions using Melody Search Optimization Algorithms combined with Extreme Softplus Learning. The performance of the proposed tumor segmentation technique is assessed based on the images acquired from the database. Our experiments have demonstrated that improved segmentation can help avoid the next level of danger. Segmentation metrics were calculated and compared to manual depictions, such as dice similarity, Jaccard index, and percentage of relative error (RE %).