BRAIN CANCER SEGMENTATION IN MRI USING FULLY CONVOLUTIONAL NETWORK WITH THE U-NET MODEL

The manual segmentation of brain tumors from magnetic resonance (MR) images represents a formidable challenge, imposing significant demands on the time and expertise of medical professionals. This study addresses the complexity of sematic segmentation in brain tumor detection, acknowledging the necessity for meticulous preprocessing and post-processing procedures. The proposed approach leverages the power absolutely Fully Convolutional Network with the U-Net model architecture, emphasizing the critical role of segmentation in cases where accurate and timely clinical diagnosis is pivotal for patient survival. The intricacies of brain tumor detection demand an advanced neural network architecture capable of discerning subtle details in MR images. By employing a FCN, the main aim is to streamline the segmentation process, mitigating the burden on healthcare practitioners. The incorporation of the U-Net model enhances the network's ability to capture intricate spatial features, ensuring a comprehensive understanding of the tumor boundaries. This research underscores the significance of leveraging deep learning techniques in medical imaging, particularly in the condition of brain tumor detection. The proposed FCN with U-Net architecture not only demonstrates robust segmentation capabilities but also addresses the need for expeditious and accurate clinical diagnoses. The findings contribute to the ongoing efforts for bettering medical image quality analysis, offering a potential breakthrough in the realm of neuro imaging and facilitating improved patient outcomes.