Texture Classification Network Integrating Adaptive Wavelet Transform
This work addresses automated diagnosis of Graves' disease via ultrasound texture analysis, representing an incremental improvement over existing CNN methods.
The paper tackled the problem of limited texture feature extraction in CNNs for Graves' disease diagnosis by integrating adaptive wavelet transforms into ResNet18, achieving 97.27% accuracy and 95.60% recall on ultrasound datasets and 60.765% accuracy on natural image texture datasets.
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.