Proceedings of the XXth Conference of Open Innovations Association FRUCT (Apr 2024)
The Detection of Brain Tumors User Interface for MATLAB
Abstract
Background: Magnetic Resonance Imaging (MRI) becomes more popular because it improves brain and soft tissue imaging. Medical imaging areas like MRI benefit from Mathematical Morphology's strong framework for studying geometric characteristics of pictures with its emergence. Objective: The focus of this study is to influence mathematical morphology for detecting brain tumors 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 categorization technique, is further exemplified through an illustrative algorithmic flowchart. Additionally, the article delves into comprehensive pre-treatment processes, discusses both linear and kernel SVMs, and emphasizes 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. against prove the suggested method's superiority, the results are rigorously contrasted against decade-old methods. Conclusion: By harnessing the capabilities of MATLAB's graphic user interface, this study offers an innovative approach to detecting malignant brain tumors. Given the severe implications of brain tumors 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.
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