Maximizing Precision in Early Prognosis using SVM-ACO Classifier and Hybrid Optimization Techniques in MRI Brain Tumor Segmentation with Integration of Multi-Modal Imaging Data
Keywords:
MRI Brain Tumor Segmentation, SVM-ACO Classifier, Hybrid Optimization Techniques, Multi-Modal Imaging DataAbstract
The paper presents a new way to predict how brain tumors may develop using MRIs. It uses support vector machines along with ant colony optimization. This classifier combines different improvement techniques. The main goal is to increase how accurate and fast brain tumor diagnosis is. This allows doctors to act sooner and give patients better care. The research aims to fix problems with traditional segmentation methods. It uses different types of MRI scans together. These scans give a fuller picture of the tumor and its features. The SVM-ACO classifier combines support vector machines and ant colony optimization. Working together, they can better segment tumors in images. The goal is to make the process more reliable and precise. Additionally, hybrid methods are added to refine how the model works. These involve strategically using optimization methods together. They enhance how accurately different parts are identified and make separating everything out smoother. The end result is a clearer picture of where tumors are located. The proposed plan is especially helpful for early prediction, as it allows exact identification and description of brain growths based on various imaging qualities. Combining different types of data makes sure a more delicate comprehension of growth form, improving the classifier's capacity to differentiate between growth and typical tissue. The examination discoveries offer expect advancing the field of restorative picture investigation and add to creating dependable devices for early conclusion and anticipation in mind growth cases. This comprehensive methodology has the potential to altogether impact clinical choice making and at last enhance patient results in the territory of neuro-oncology.
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