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A hybrid framework that combines generative adversarial networks (GANs), convolutional neural networks (CNNs), and Vision Transformers (ViTs) for accurate tumor classification demonstrates the potential of advanced DL models to strengthen MRI-based tumor diagnosis and support more reliable clinical decision-making.
This work proposes a supervised approach that combines both CNN and the vision transformer (ViT) to predict a dense displacement field (DDF) in 3D medical images and achieves promising results compared to previous work based on only CNNs.