Progress in Brain Tumour Disease Detection Using Advanced Object Recognition and Artificial Intelligence

Abstract — Brain tumours represent a critical and life-threatening medical condition that demands accurate and timely diagnosis for effective treatment and improved survival rates. Traditional diagnostic approaches, primarily reliant on radiologists' interpretations of MRI and CT scans, are often time-consuming, subjective, and heavily dependent on clinical expertise. This paper explores the application of YOLOv9, an advanced real-time object detection algorithm, to automate and enhance brain tumour identification in medical imaging, leveraging recent strides in Artificial Intelligence (AI) and deep learning. The YOLOv9 model introduces novel architectural innovations, including Programmable Gradient Information (PGI) and the General Efficient Layer Aggregation Network (GELAN), contributing to high detection precision and computational efficiency. Our implementation achieves a mean Average Precision (mAP) of 94.6% and a precision rate of 92.5%, demonstrating the model’s robustness in distinguishing tumor regions from complex brain scan imagery. Despite challenges such as data heterogeneity, high-resolution imaging requirements, and the need for extensive computational resources, YOLOv9 shows significant potential as a clinical support tool in neuro-oncology. Furthermore, this research discusses future opportunities including the integration of edge computing and Iot-enabled diagnostic systems, which could enable real-time, remote tumor detection and support resource-constrained medical settings. The findings underscore the growing role of AI in driving scalable, efficient, and accurate diagnostic technologies in modern healthcare. Keywords— YOLOv9, Brain Tumour Detection, Deep Learning, SVM, CNN, Medical Imaging