Colored MRI biomedical image tumor classification and segmentation based on transfer learning of modified Y-Net

Abstract

By incorporating the colored MRI identification synthesis into the MRI segmentation model with transfer learning AI Y-Net, this study clearly shows the high potential of a multidisciplinary system-level study for diagnoses. This way, such a system can provide integrity of the goal without compromising the quality of each one and saving time consumption. Another alternative to such integration is to be used for enhancement and segmentation that is accurate and robust to the variabilities in scanner and acquisition protocols. System Level Simulator is the deep learning based on Kearse AI deep learning network specified to Y-VGG16 net results of outstanding performance in medical image segmentation. Based on the literature, there are different AI models for the diagnosis system, which are different of what is proposed in this paper. A partial-frozen network is applied to the U-net to compare results between different fine-tuning FT strategies. The network operation is also evaluated depending on the dataset size, showing the importance of the combination of dataset, TL and data augmentation (DA). Transfer learning (TL) helps us for MRI medical image segmentation deep learning with more accurate performances of the TL technique. The system hybrid the Y-Net architecture with Transfer learning to reduce the domain-shift effect in brain MRI segmentation results of the automated deep learning segmentation approach.