Enhancing brain tumor segmentation using attention based convolutional UNet on MRI images

Precise segmentation of brain tumors is essential for efficient diagnosis and therapy planning. While current automated methods frequently fail to capture complicated tumor shapes, traditional manual methods are laborious, subjective, and unpredictable. These issues are addressed by the suggested Attention-based Convolutional U-Net (ACU-Net) model, which incorporates attention processes into the U-Net architecture. The objective is to enhance the degree of precision and dependability of the tumor's edge delineation by proposing and testing the ACU-Net model-based brain tumor segmentation on MRI data. The research framework consists of data acquisition from the BraTS 2018 MRI data set. The first processing steps carried out in this study were the normalization of acquired data, spatial resolution, and augmentation of the obtained data. ACU-Net is a model developed with the use of attention gates and has been trained with dice and cross-entropy losses. Precision, recall, dice similarity coefficient (DSC), and intersection over union (IoU) are the performance measures used in the proposed ACU-Net and compared with the basic benchmark models, including U-Nets and convolutional neural networks (CNNs). The model of ACU-Net was shown to be most effective in brain tumor segmentation, and the dice scores were 94.04% for Whole Tumor (WT), 98. 63% for Tumor Core (TC) and 98.77% for Enhancing Tumor (ET). The proposed ACU-Net performed better than baseline models, showing the high capacity of the current approach to segment various classes of tumors. The model ACU-Net enhances brain tumor segmentation, acting as a reliable tool for clinical applications. These findings confirm that attention mechanisms improve the accuracy and robustness of medical image segmentation.