FFCNet: Fourier Transform-Based Frequency Learning and Complex Convolutional Network for Colon Disease Classification

TL;DR

FFCNet introduces a Fourier transform-based complex network that enhances colon disease classification by decoupling brightness and capturing long-range features, outperforming previous methods on a large dataset.

Contribution

The paper proposes a novel Fourier-based complex network that combines frequency learning with complex convolution to improve colon disease classification accuracy.

Findings

Achieved 86.35% accuracy on colonoscopy images.

Outperformed previous state-of-the-art methods by 4.46%.

Effectively decoupled brightness and enhanced feature learning.

Abstract

Reliable automatic classification of colonoscopy images is of great significance in assessing the stage of colonic lesions and formulating appropriate treatment plans. However, it is challenging due to uneven brightness, location variability, inter-class similarity, and intra-class dissimilarity, affecting the classification accuracy. To address the above issues, we propose a Fourier-based Frequency Complex Network (FFCNet) for colon disease classification in this study. Specifically, FFCNet is a novel complex network that enables the combination of complex convolutional networks with frequency learning to overcome the loss of phase information caused by real convolution operations. Also, our Fourier transform transfers the average brightness of an image to a point in the spectrum (the DC component), alleviating the effects of uneven brightness by decoupling image content and brightness. Moreover, the image patch scrambling module in FFCNet generates random local spectral blocks, empowering the network to learn long-range and local diseasespecific features and improving the discriminative ability of hard samples. We evaluated the proposed FFCNet on an in-house dataset with 2568 colonoscopy images, showing our method achieves high performance outperforming previous state-of-the art methods with an accuracy of 86:35% and an accuracy of 4.46% higher than the backbone. The project page with code is available at https://github.com/soleilssss/FFCNet.