AI-Based Brain Tumor Detection and Classification Using YOLOv9 on MRI Images

International Journal of Emerging Research in Science, Engineering, and Management
Vol. 2, Issue 3, pp. 149-155, March 2026.

https://doi.org/10.58482/ijersem.v2i3.19

This work is licensed under a Creative Commons Attribution 4.0 International License.

AI-Based Brain Tumor Detection and Classification Using YOLOv9 on MRI Images

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M Soniya

N Dhanalakshmi

 Department of ECE, Gokula Krishna College of Engineering, Sullurpet, India.

Abstract: Brain tumor detection is a critical task in medical diagnosis, requiring high accuracy and early identification to improve patient outcomes. Traditional diagnostic methods, such as biopsy, are invasive and may lead to complications, necessitating the use of non-invasive imaging techniques like Magnetic Resonance Imaging (MRI). In this study, an AI-based framework is proposed for the detection and classification of brain tumors using deep learning. The dataset consists of annotated MRI images categorized into four classes: glioma, meningioma, pituitary tumor, and no tumor. Roboflow is utilized for image annotation and preprocessing, while the YOLOv9 algorithm is employed for efficient object detection and segmentation. The model is trained and evaluated using performance metrics such as Mean Average Precision (mAP), precision, recall, and F1-score. Experimental results demonstrate that the proposed model achieves high accuracy and fast detection speed, making it suitable for real-time clinical applications. The study highlights the effectiveness of YOLOv9 in medical image analysis and its potential to assist radiologists in accurate and timely diagnosis.

Keywords: Brain Tumor Detection, Deep Learning, YOLOv9, MRI, Medical Image Processing.

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This article is part of Volume 2, Issue 3 (March 2026)