Accurate segmentation of colorectal polyps in colonoscopy images is essential for the early diagnosis and prevention of colorectal cancer. However，colonoscopy images often exhibit blurred boundaries，irregular shapes，and large-scale variations，making automated segmentation highly challenging. Conventional convolutional neural network （CNN）-based methods struggle to capture long-range dependencies，often leading to inaccurate boundary localization and missed detection of small lesions. Although Transformer-based models improve global context modeling，they present quadratic computational complexity and high memory consumption when processing high-resolution medical images. The Mamba architecture，built upon selective state space models，has recently shown strong potential for efficient sequence modeling with linear computational complexity. Despite its advantages，directly applying Mamba-based models to polyp segmentation remains challenging due to its limited capability to extract local features and insufficient multiscale feature interaction. Aiming to address these issues，this paper proposes a multilevel and multiscale feature fusion enhanced network based on the Mamba architecture （MMF-MambaNet），which intends to improve segmentation accuracy while maintaining computational efficiency.

MMF-MambaNet adopts an encoder-decoder architecture and employs the visual state space model，a variant of Mamba，as the backbone to efficiently capture long-range contextual dependencies. Aiming to enhance feature diversity and robustness，a local sequence-wise style transformation augmentation module is integrated into the backbone. This module performs stochastic re-parameterization by perturbing the mean and variance of feature distributions，thereby improving model generalization under varying imaging conditions. Three specialized modules are also incorporated to strengthen multi-scale representation and boundary perception. First，a cross-scale aware self-attention （CASA） module is introduced to facilitate hierarchical feature interaction by using deep semantic features to refine shallow features. By combining channel and spatial attention mechanisms，CASA effectively fuses multilevel features while preserving fine boundary details. Second，an adaptive detail enhancing module （ADEM） is designed to improve structural representation across different scales. Using an anchor-scale fusion strategy，this module adaptively integrates information from different scales to construct highly comprehensive detail representations of polyps，thereby enhancing boundary structures and improving the segmentation of small lesions. Third，an adaptive global-local integration （AGLI） gating module is introduced in the decoder stage. This lightweight mechanism adaptively combines global semantic features with local detailed features from skip connections，enabling the model to emphasize reliable regions while suppressing background interference. Furthermore，MMF-MambaNet introduces a reverse feature fusion pathway that progressively combines deep semantic features with shallow spatial information through upsampling and feature aggregation. A pyramid pooling module is also employed to capture multiscale global contextual information，thereby improving semantic consistency in the segmentation results. The model is optimized using a hybrid binary cross-entropy and dice loss，which balances pixel-level classification accuracy with region-level overlap while alleviating class imbalance in medical image segmentation.

Extensive experiments were conducted on five publicly available colorectal polyp segmentation datasets： CVC-300，CVC-ClinicDB，Kvasir-SEG，CVC-ColonDB，and ETIS-LaribPolypDB. The proposed MMF-MambaNet was compared with eight state-of-the-art segmentation methods，including PraNet，VM-UNetV2，and Polyp-Mamba. Experimental results demonstrate that MMF-MambaNet achieves competitive and stable performance across multiple datasets. Compared with the VM-UNet baseline，MMF-MambaNet improves the mean Dice coefficient （mDice） by 1.1%，5.0%，4.2%，9.1%，and 2.6% on the CVC-300，CVC-ClinicDB，Kvasir-SEG，CVC-ColonDB，and ETIS-LaribPolypDB datasets，respectively. On the challenging CVC-ColonDB dataset，the proposed model further outperforms VM-UNetV2，achieving improvements of 4.6% in mDice and 6.2% in mean intersection over union （mIoU）. Visual comparisons show that MMF-MambaNet can accurately delineate polyp boundaries even under challenging cases，such as low contrast，irregular shapes，and small lesion sizes. In addition to segmentation accuracy，the model also demonstrates favorable computational efficiency. With 18.84 M parameters and 5.74 GFLOPs，MMF-MambaNet maintains a relatively lightweight architecture while achieving an inference speed of 57.58 frames per second. Ablation studies further confirm the effectiveness of the proposed modules，revealing that CASA，ADEM，and AGLI each contribute to enhanced segmentation accuracy and improved boundary preservation.

This study presents MMF-MambaNet，a Mamba-based network that integrates multilevel and multiscale feature fusion mechanisms designed for colorectal polyp segmentation. By combining efficient sequence modeling with adaptive feature interaction strategies，the proposed method effectively addresses the challenges such as blurred boundaries and scale variations in colonoscopy images. Experimental results demonstrate that MMF-MambaNet exhibits strong segmentation performance while maintaining lightweight computational complexity，making it a promising approach for computer-aided colonoscopy diagnosis and assisting the early detection of colorectal cancer.