MTM3D:3D Medical Image Analysis Model Integrating Mamba and Improved TTM

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Biomedical imaging is crucial in the diagnosis and treatment of various diseases. The application of deep learning methods to medical image analysis can enhance the readability of medical images and provide more reliable support for clinical decision-making. However, traditional medical image processing methods are limited in effectively capturing spatial features and complex structural information in 3D images, especially when handling complex 3D medical images generated by different imaging modalities. This often challenges the accuracy and generalization ability of the model. To address this challenge, an MTM3D model is proposed for medical image classification tasks. This model combines the excellent performance of the Mamba model in complex sequential tasks with the external memory storage function of the improved Token Turning Machine (TTM) network. By introducing a cyclic chain storage structure, MTM3D enables effective interaction of features from different spatial structures within memory units, thus enhancing its ability to capture complex spatial relationships. Furthermore, the incorporation of Mamba further strengthens the interaction between the memory and processing units, strengthening the generalization capability of the model and enhancing its performance across different medical imaging datasets. Experimental results on the MedMNIST v2 dataset demonstrate that MTM3D exhibits outstanding capabilities in understanding medical images. Compared with the current best medical image analysis networks, MTM3D improves the average Accuracy (ACC) by 3.97% and the average Area Under the Curve (AUC) by 2.00%, fully showcasing its tremendous potential in interpreting medical images and assisting healthcare professionals in diagnosis and treatment planning.