A System for Biomedical Image Processing of Brain Tumor via Segmentation and Pattern Recognition

Advancements in the biomedical field have led to remarkable progress in the early diagnosis and treatment of tumors, particularly in the context of brain tumors. However, accurately identifying and differentiating brain tumor cells from normal tissues remains a critical challenge, as manual classification methods often yield inaccurate results. To address this issue, biomedical image processing technology has emerged as a valuable tool, offering numerous algorithms and models to enhance the precise detection of brain tumors. One such algorithm, the random forests (RF) algorithm, has been employed in this study to propose a new technique for segmenting brain tumors. This technique utilizes magnetic resonance imaging (MRI) of the brain and evaluates its performance based on metrics such as dice similarity coefficient (DSC) and algorithm accuracy (ACC). Comparing the outcomes of this proposed RF machine learning algorithm with existing segmentation approaches, the results indicate promising improvements. The collected data demonstrates that the proposed RF algorithm performs favorably in accurately segmenting brain tumors, showcasing its potential to enhance tumor detection in clinical settings.