BTIS-NET, SERESU-NET, and AGRESU-NET: A Comparative Study of Deep Learning Architectures for Brain Tumor Segmentation - Maskawade, Subhash; Yele, Sachin (Journal of Neonatal Surgery, 2025)
One of the most important steps in clinical diagnosis and treatment planning is the automated and precise segmentation of brain tumors using magnetic resonance imaging (MRI). Deep learning-based solutions are required since manual segmentation is laborious and subject to inter-observer variability. Three sophisticated convolutional neural network designs for brain tumor segmentation are compared in this paper: AGRESU-NET (Attention-Gated Residual U-Net), SERESU-NET (Squeeze-and-Excitation Residual U-Net), and BTIS-NET (Brain Tumor Inception Segmentation Network). The BraTS 2021 dataset, which consists of multi-modal MRI images with tumor subregion annotations, was used to assess the models. Sensitivity, Hausdorff Distance (HD), and Dice Similarity Coefficient (DSC) were used to evaluate performance. With an average DSC of 0.91 for total tumor segmentation and decreased HD, the experimental findings showed that AGRESU-NET performed better than the other models, demonstrating the usefulness of residual encoding and attention processes in segmenting complicated tumor geometries. The paper emphasizes how architectural developments have improved automated brain tumor segmentation systems' accuracy and dependability