Transactions on Machine Intelligence 2821-1693 8 1 2025 03 01 Improving Medical Image Segmentation Using a Hybrid ResUNet-Transformer Architecture for Liver Tumor Detection 57 68 224190 10.47176/TMI.2025.57 EN N. Shakeri Department of Biomedical Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran O. Rahmani Seryasat Assistant Professor, Department of Electrical Engineering, Shams Higher Education Institute, Gorgan, Iran 0000-0002-9289-6128 Journal Article 2025 01 22 In this research, a new method for segmentation of medical images is presented using a combination of ResUNet architecture and transformer layers. The main objective of this study is to improve the accuracy and efficiency of segmentation models in identifying liver tumors from medical images. In this method, the ResUNet50 architecture is used as the encoder for extracting deep features from images, and transformer layers have been added to the model to enhance the model's ability to understand spatial and channel relationships between features. Then, a decoder section with U-Net structure has been designed to reconstruct the predicted maps. To evaluate the proposed method, a dataset of medical images related to liver tumors was used. The experimental results show that the proposed method performs better compared to baseline models according to metrics such as accuracy, Jaccard coefficient (IoU), and Dice coefficient, and has achieved an average accuracy of 92.5%, Jaccard coefficient of 75.4%, and Dice coefficient of 83.1%. These results indicate that the combination of ResUNet and transformer architecture can provide an effective and powerful tool for segmentation of medical images and more accurate identification of liver tumors. In the future, using more diverse data and applying further optimization techniques can improve the efficiency of this model. https://www.tmachineintelligence.ir/article_224190_e447ca5bf8514a4ccead026b5a5ea69a.pdf