EffTransUNet: One Method for Medical ImageTasks Based on TransUNet

With the rapid advancement of deep learning technologies, their applications have expanded across various fields, particularly in medical image analysis, where segmentation remains a critical task. This study proposes a modified version of the TransUNet model, named EffTransUNet, to enhance segmentation performance on the Synapse multi-organ segmentation dataset and the LeftAtrium dataset. Additionally, the model is applied to the Brain Tumor dataset for image classification. Experimental results show that EffTransUNet achieves an accuracy of 91.33% on the LeftAtrium dataset and a classification accuracy of 99.69% on the Brain Tumor dataset. These findings demonstrate that the proposed model effectively improves segmentation performance and accurately classifies brain tumour MRI images, indicating good generalization ability.