Advancing brain tumor diagnosis with Deep Learning-Driven image segmentation techniques

Zeinab Khadra, Ammar Asad, Mohammed Hammoud, Ola Haydar, Sergey Sergeevich Lupin

Abstract


Early diagnosis of brain tumors is critical for improving patient outcomes. This study presents a novel AI-driven framework for the early detection of brain tumors utilizing magnetic resonance imaging (MRI) scans. The core of the framework is a U-Net, a deep learning model specifically designed for image segmentation. Leveraging a substantial dataset of annotated MRI images, the U-Net is trained to accurately segment and identify tumor regions within the brain, even in their early stages where subtle abnormalities may be present. The proposed framework is evaluated on a diverse dataset of MRI scans, demonstrating its capability to achieve high accuracy in tumor segmentation and detection. Moreover, we explore the potential of incorporating explainable AI techniques to provide insights into the model’s decision-making process, thereby enhancing the clinical interpretability of the results. Our findings suggest that the AI-powered framework, based on the Unet architecture, holds substantial promise for improving the early detection of brain tumors, potentially leading to better patient management and prognosis.

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References


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