Brain Tumor Detection Using MATLAB: Algorithm Implementation and Optimization

In this article, we explore the implementation of brain tumor detection using MATLAB. Brain tumor detection represents a critical medical task that enables early identification of tumors and facilitates necessary treatment interventions. Using MATLAB for brain tumor detection assists medical professionals in achieving more accurate patient diagnoses while improving diagnostic efficiency. This article demonstrates how to develop brain tumor detection algorithms in MATLAB, including image preprocessing using imread and imresize functions, segmentation techniques with watershed algorithm or region-growing methods, and feature extraction employing statistical and texture analysis. We discuss algorithm optimization approaches such as parameter tuning with optimset functions and performance evaluation using confusion matrices to enhance both accuracy and computational efficiency. Furthermore, we examine MATLAB's capabilities for data visualization and analysis through imshow, plot, and histogram functions to better understand tumor characteristics and trends. The implementation covers key MATLAB toolboxes including Image Processing Toolbox for morphological operations and Statistics and Machine Learning Toolbox for classification algorithms. In summary, this article provides a comprehensive guide to MATLAB-based brain tumor detection methodologies, aiming to deliver valuable references for both medical research and practical clinical applications.