A Vision Transformer-Enhanced ResU-Net50 Model for Accurate MRI Brain Tumor Segmentation Using DBO Optimization
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Abstract
For precise evaluation and therapeutic planning, automatic brain cancer segmentation from MRI scans is essential. In this work, a hybrid deep learning framework for image segmentation comprising a Vision transformer and ResU-Net50 is proposed. It combines the strong local feature extraction capabilities of ResNet50 with the global representation learning capabilities of a Vision Transformer embedded in a U-Net framework. To improve mask quality and prediction stability, a preprocessing pipeline powered by Dung Beetle Optimization (DBO) is used to optimize thresholding, morphological enhancement, and test-time augmentation. Comparing DBO-based preprocessing to non-optimized preprocessing, experimental results on a T1-CE MRI dataset with 3064 annotated images show that the former greatly enhances segmentation performance, resulting in increases of 2.3% in precision, 1.9% in Dice Coefficient, and 3.2% in Jaccard Index. With Dice scores of 0.9630, 0.9873, and 0.9862 for pituitary, glioma, and meningioma tumors, respectively, the suggested Vision-ResU- Net50 model further achieves exceptional tumor-wise segmentation results, outperforming cutting-edge techniques like Edge U-Net, Improved U-Net, CNN, and deep learning models by margins of 417% in Dice and 515% in Jaccard Index, the suggested approach is a highly efficient and dependable for clinical brain tumor analysis.
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