Texture Classification Network Integrating Adaptive Wavelet Transform
Su-Xi Yu Jing-Yuan He Yi Wang Yu-Jiao Cai Jun Yang Bo Lin Wei-Bin Yang Jian Ruan
Graves' disease is a common condition that is diagnosed clinically by
determining the smoothness of the thyroid texture and its morphology in
ultrasound images. Currently, the most widely used approach for the automated
diagnosis of Graves' disease utilizes Convolutional Neural Networks (CNNs) for
both feature extraction and classification. However, these methods demonstrate
limited efficacy in capturing texture features. Given the high capacity of
wavelets in describing texture features, this research integrates learnable
wavelet modules utilizing the Lifting Scheme into CNNs and incorporates a
parallel wavelet branch into the ResNet18 model to enhance texture feature
extraction. Our model can analyze texture features in spatial and frequency
domains simultaneously, leading to optimized classification accuracy. We
conducted experiments on collected ultrasound datasets and publicly available
natural image texture datasets, our proposed network achieved 97.27% accuracy
and 95.60% recall on ultrasound datasets, 60.765% accuracy on natural image
texture datasets, surpassing the accuracy of ResNet and conrming the
effectiveness of our approach.