Object Detection for Brain Cancer Detection and Localization

Abstract

Brain cancer is acknowledged as one of the most aggressive tumors, with a significant impact on patient survival rates. Unfortunately, approximately 70% of patients diagnosed with this malignant cancer do not survive. This paper introduces a method designed to detect and localize brain cancer by proposing an automated approach for the detection and localization of brain cancer. The method utilizes magnetic resonance imaging analysis. By leveraging the information provided by brain medical images, the proposed method aims to enhance the detection and precise localization of brain cancer to improve the prognosis and treatment outcomes for patients. We exploit the YOLO model to automatically detect and localize brain cancer: in the analysis of 300 brain images we obtain a precision of 0.943 and a recall of 0.923 in brain cancer detection while, relating to brain cancer localization, an mAP_0.5 equal to 0.941 is reached, thus showing the effectiveness of the proposed model for brain cancer detection and localization.

Keywords

• brain
• object detection
• YOLO
• deep learning
• classification