Automated Detection of Lower-Grade Gliomas Using Deep Learning with UNet and EfficientNet-B7Detecting lower-grade gliomas (LGGs) remains a significant challenge in neuro-oncology due to their complex nature and variable clinical behaviors. Accurate identi

Abstract

Detecting lower-grade gliomas (LGGs) remains a significant challenge in neuro-oncology due to their complex nature and variable clinical behaviors. Accurate identification and classification of LGGs are crucial for devising effective treatment strategies and improving patient outcomes. This study presents an innovative approach to LGG detection leveraging advanced deep learning techniques, outperforming traditional image segmentation methods. The research emphasizes the use of the UNet model, enhanced with an EfficientNet B7 backbone, to achieve superior accuracy in automatic LGG prediction. By integrating these cutting-edge technologies, the proposed framework not only streamlines the detection process but also enhances the precision of diagnosis. This approach provides valuable insights that can significantly aid in the early identification and management of LGGs. Furthermore, the proposed method focuses on overcoming limitations associated with traditional techniques, such as manual segmentation inaccuracies and computational inefficiencies. The adoption of deep learning enhances the model's ability to analyze intricate patterns and subtle variations in medical imaging, leading to more reliable and consistent results. By advancing the automation of LGG detection, this research contributes to the ongoing development of diagnostic tools in neuro-oncology, potentially reducing diagnostic delays and enabling personalized treatment approaches. The findings pave the way for future advancements in integrating artificial intelligence into medical imaging and neuro-oncology practices