A NEW O-NET MODEL FOR AUTOMATED BRAIN TUMOR SEGMENTATION ON MAGNETIC RESONANCE IMAGES

Cancer is a major contributor to global mortality rates, with brain tumors being among the most dangerous types. Early diagnosis and appropriate treatment significantly impact patients' survival rates and quality of life. Moreover, accurately determining the tumor type is crucial for treatment planning and the development of personalized treatment strategies. Therefore, a reliable and effective imaging and analysis method is required to classify brain tumors accurately. In recent years, advancements in deep learning techniques have led to significant progress in medical imaging. This study introduces O-Net, a deep learning model specifically designed to automate brain tumor segmentation. Based on the U-Net architecture, O-Net exhibits a sophisticated structure and depth, enabling superior performance. Experimental evaluations using brain Magnetic Resonance Imaging (MRI) revealed that the O-Net model outperformed other studies in the literature. The model achieved a high accuracy rate with a 90% Dice score. These results demonstrate that O-Net is a reliable tool for the accurate detection and segmentation of brain tumors. The advanced version of the O-Net model can be effectively utilized in future clinical applications for the diagnosis and treatment of brain tumors.