InceptionMamba: A Lightweight and Effective Model for Medical Image Classification Revealing Mamba’s Low-Frequency Bias

In previous research on medical image classification, CNNs cannot capture long-range dependencies with local receptive fields and result in poor classification performance. Transformer-based models are limited by the quadratic computational complexity of the self-attention mechanism, especially when processing high-resolution medical images. It is difficult to deploy them in limited computational settings without sacrificing performance. Mamba-based models have attracted a lot of interests in computer vision due to their linear computation complexity. Despite their low FLOPs, Mamba-based models with less parameters perform sub-optimally in image classification tasks. To overcome the limitations of Mamba-based models, we propose InceptionMamba, a model that combines lightweight design with high accuracy for medical image classification tasks. Inspired by the impressive performance of the Inception architecture at a relatively low computational cost, we introduce Inception modules to the Mamba-based model. Meanwhile, a channel attention mechanism is employed to improve performance. Additionally, we conduct an in-depth analysis of the modeling capabilities of State Space Model (SSM) from the perspective of frequency response, revealing that it is better suited for medical images dominated by low-frequency components rather than natural images dominated by high-frequency information. InceptionMamba demonstrates competitive performance on medical image classification tasks, surpassing most state-of-the-art methods. The source code is publicly available at https://github.com/pepper1329/InceptionMamba.