The Detection of Brain Tumors User Interface for MATLAB

Background: Magnetic Resonance Imaging (MRI) has become more popular because it improves brain and soft tissue imaging. Medical imaging areas like MRI benefit from Mathematical Morphology’s robust framework for studying geometric characteristics of pictures with their emergence. Objective: This study focuses on influencing mathematical morphology for detecting brain tumours and cancer cells in MRI images, aiming to significantly improve diagnostic accuracy and treatment strategies using the MATLAB graphic user interface. Methods: The methodology encompasses three primary steps: 1) Preprocessing, which includes feature extraction and reduction; 2) Training kernel Support Vector Machines (SVM); and 3) Processing new MRI images through the trained SVM for predictions. This approach, rooted in a proven categorisation technique, is further exemplified through an illustrative algorithmic flowchart. Additionally, the article delves into comprehensive pre-treatment processes, discusses linear and kernel SVMs, and emphasises the importance of K-fold cross-validation to counteract overfitting. Results: The current study on 160 MRI images using SVMs with different Kernels showed good linear separability in feature spaces. The results are rigorously contrasted against decade-old methods to prove the suggested method’s superiority. Conclusion: This study offers an innovative approach to detecting malignant brain tumours by harnessing the capabilities of MATLAB’s graphic user interface. Given the severe implications of brain tumours on neurological health and quality of life and the prohibitive costs of treatments, such advancements in diagnostic methods signify a monumental stride in medical imaging and diagnosis.