ENHANCED BRAIN DISORDER DETECTION THROUGH YOLOV5 IN MEDICAL IMAGE ANALYSIS

Abstract

Abstract : This research work presents a novel approach to medical image analysis, introducing a YOLO-based instance segmentation model specifically tailored for neurological conditions in brain scans. By leveraging state-of-the-art deep learning techniques, the proposed model aims to not only detect but also precisely delineate anomalies such as tumors and lesions within these images. Through the adaptation of the YOLO architecture for instance segmentation, we enable real-time detection and accurate outlining of abnormalities, providing clinicians with valuable insights for diagnosis and treatment planning. The significance of this development lies in its potential to revolutionize neurology by enhancing the accuracy and efficiency of medical image analysis, ultimately leading to improved patient care and outcomes. With the automation capabilities of our model, healthcare professionals can streamline their workflow, dedicating more time to patient interaction and decision- making. This research work  represents a significant step forward in the integration of  deep learning technologies into clinical practice, promising to advance diagnostic capabilities and transform patient care in neurology and beyond.