Abstract
In medical imaging diagnosis, the identification of normal liver, fatty liver, and cirrhosis is often challenging due to subtle morphological and density differences. Previous studies have used CNNs, MLPs or transformers to extract lesion features. However, CNN's global representation is limited, while MLPs and transformers have insufficient local modeling, resulting in insufficient lesion information mining. Therefore, this article proposes a hybrid network CMT-Net, which unifies the local perception of CNNs, high-dimensional mapping of MLPs, and global dependencies of Transformers into a single architecture, significantly improving the accuracy of CT liver three classification. The core components of CMT-Net include an efficient transformer (ET) module, which focuses on extracting local feature details while progressively integrating global information, significantly enhancing the model feature representation and generalization capabilities. Additionally, this paper introduces a Hybrid MLP (HM) module that combines Token-Mixing MLP and Channel-Mixing MLP to achieve deep fusion of spatial and channel information, further improving feature extraction. To validate the proposed algorithm, extensive experiments were conducted on a dataset of three liver diseases collected from the Imaging Department of Urumqi People's Hospital. The results demonstrate that CMT-Net achieves outstanding performance in classifying normal liver, fatty liver, and cirrhosis. These findings not only provide an effective tool for precise liver disease diagnosis but also offer new directions for deep learning model design in medical image classification tasks.