• AVANTI KUMARI A
• MIZPAH QUEENY R
• BANUMATHI B S
• HARINI S

The abstract of MRI brain tumor classification employing machine learning with Support Vector Machine (SVM) algorithm unfolds a comprehensive methodology aimed at precise and clinically relevant tumor characterization. Commencing with a diverse dataset of MRI images annotated with tumor types, the approach encompasses meticulous preprocessing to standardize image formats and extract discriminative features indicative of tumor morphology. By harnessing SVM's capability to discern intricate patterns in high-dimensional feature spaces, the algorithm adeptly learns to classify tumors based on the extracted features, refining decision boundaries through iterative training and hyper parameter optimization. Evaluation metrics such as accuracy, precision, recall, and F1-score provide quantitative insights into classification performance, while visual interpretation aids in qualitative analysis of results. Through this holistic framework, MRI brain tumor classification with SVM delivers accurate and reliable outcomes, furnishing clinicians with indispensable diagnostic tools to tailor treatment strategies in the realm of neuro-oncology.

Brain Tumor Detection, Machine learning, Feature Extraction, Image Segmentation, Age Analysis, Accuracy.

"MRI BRAIN TUMOR CLASSIFICATION AND SEGMENTATION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 5, page no.g763-g771, May-2024, Available :http://www.jetir.org/papers/JETIR2405696.pdf

"MRI BRAIN TUMOR CLASSIFICATION AND SEGMENTATION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 5, page no. ppg763-g771, May-2024, Available at : http://www.jetir.org/papers/JETIR2405696.pdf