Optimizing Brain Tumor Segmentation with Advanced Superpixel Techniques in Conjunction with Deep Learning Methods

Brain tumor segmentation in medical imaging is crucial for precise diagnosis and treatment planning. To address these issues, our research leverages deep learning techniques and introduces an innovative approach that combines various key components. Our methodology includes preprocessing, superpixel segmentation, feature extraction, and the use of a Convolutional Neural Network (CNN) to achieve precise brain tumor segmentation. This approach is driven by the necessity for improved accuracy and efficiency, especially with diverse clinical data and tumor types, like the challenging BraTS 2018 dataset, consisting of 1070 T1W-CE MRI images. This approach using metrics such as the Dice score, sensitivity, and specificity, which demonstrate the superior performance of our superpixel model in brain tumor segmentation. This proposed method yields exceptional results, with a Dice score of 97.06%, sensitivity of 99.05%, and specificity of 99.83%, highlighting its accuracy and effectiveness in tumor segmentation when compared to existing methods such as UNet, Kapur entropy, and rough set theory. This research significantly advances the state of the art and offers great promise for enhancing medical image analysis in this critical field.