DFE-IANet: A Method for Polyp Image Classification Based on Dual-domain Feature Extraction and Interaction Attention

TL;DR

This paper introduces DFE-IANet, a novel polyp image classification network that combines spectral transformation and feature interaction attention to improve accuracy and efficiency, achieving state-of-the-art results on the Kvasir dataset.

Contribution

The paper proposes a new network architecture integrating multi-scale frequency domain feature extraction and interaction attention, enhancing feature detail capture and critical region focus.

Findings

Achieves 93.94% Top-1 accuracy on Kvasir dataset.

Outperforms ViT, ResNet50, and VMamba in accuracy.

Maintains high efficiency with only 4 million parameters.

Abstract

It is helpful in preventing colorectal cancer to detect and treat polyps in the gastrointestinal tract early. However, there have been few studies to date on designing polyp image classification networks that balance efficiency and accuracy. This challenge is mainly attributed to the fact that polyps are similar to other pathologies and have complex features influenced by texture, color, and morphology. In this paper, we propose a novel network DFE-IANet based on both spectral transformation and feature interaction. Firstly, to extract detailed features and multi-scale features, the features are transformed by the multi-scale frequency domain feature extraction (MSFD) block to extract texture details at the fine-grained level in the frequency domain. Secondly, the multi-scale interaction attention (MSIA) block is designed to enhance the network's capability of extracting critical features. This block introduces multi-scale features into self-attention, aiming to adaptively guide the network to concentrate on vital regions. Finally, with a compact parameter of only 4M, DFE-IANet outperforms the latest and classical networks in terms of efficiency. Furthermore, DFE-IANet achieves state-of-the-art (SOTA) results on the challenging Kvasir dataset, demonstrating a remarkable Top-1 accuracy of 93.94%. This outstanding accuracy surpasses ViT by 8.94%, ResNet50 by 1.69%, and VMamba by 1.88%. Our code is publicly available at https://github.com/PURSUETHESUN/DFE-IANet.