A Supervised ML Applied Classification Model for Brain Tumors MRI.

Area of Science:

• Medical imaging analysis
• Artificial intelligence in healthcare
• Oncology research

Background:

• Brain tumors arise from uncontrolled abnormal cell growth.
• Magnetic resonance imaging (MRI) provides high-quality medical images.
• Machine learning can enhance MRI diagnostic accuracy for tumors.

Purpose of the Study:

• To develop and evaluate a supervised machine learning model for brain tumor classification using MRI.
• To analyze the performance of the model in terms of accuracy, precision, sensitivity, and F1 score.
• To compare the proposed model's classification accuracy against existing methods.

Main Methods:

• A supervised machine learning training and testing model was applied.
• The model was used to classify and analyze features from brain tumor MRI scans.
• Performance metrics included accuracy, precision, sensitivity, and F1 score.

Main Results:

• The proposed machine learning model achieved over 95% accuracy.
• The model demonstrated high performance in classifying brain tumor features.
• Results indicate superior accuracy compared to other existing methods.

Conclusions:

• The developed supervised machine learning model effectively classifies brain tumors from MRI scans.
• The model offers a significant improvement in diagnostic accuracy for brain tumor analysis.
• This approach holds promise for automated and precise brain tumor diagnosis.