Detecting Brain Tumors Using an Adapted DE Algorithm with the Otsu Technique

Abstract

Brain tumors require precise and prompt detection techniques because they pose a serious challenge to medical diagnosis and treatment. In order to improve brain tumor diagnosis from medical imaging data, this work presents a novel method that combines the Otsu methodology with a customized Differential Evolution (DE) algorithm. For segmentation, the Otsu approach is used for accurate thresholding, and the DE algorithm is specifically tailored to improve the process. This study assesses the efficacy of the suggested methodology through thorough testing and analysis using a variety of datasets, including MRI and CT scans. Comparing the results to traditional methods, it is clear that the accuracy of tumor detection and the quality of segmentation have significantly improved. The integrated approach exhibits favorable outcomes for advancing computer-aided diagnostic systems, offering medical practitioners a reliable means for early tumor identification and subsequent treatment planning.