Texture Classification Network Integrating Adaptive Wavelet Transform

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.

ThreshNet: An Efficient DenseNet using Threshold Mechanism to Reduce Connections

With the continuous development of neural networks in computer vision tasks, more and more network architectures have achieved outstanding success. As one of the most advanced neural network architectures, DenseNet shortcuts all feature maps to solve the problem of model depth. Although this network architecture has excellent accuracy at low MACs (multiplications and accumulations), it takes excessive inference time. To solve this problem, HarDNet reduces the connections between feature maps, making the remaining connections resemble harmonic waves. However, this compression method may result in decreasing model accuracy and increasing MACs and model size. This network architecture only reduces the memory access time, its overall performance still needs to be improved. Therefore, we propose a new network architecture using threshold mechanism to further optimize the method of connections. Different numbers of connections for different convolutional layers are discarded to compress the feature maps in ThreshNet. The proposed network architecture used three datasets, CIFAR-10, CIFAR-100, and SVHN, to evaluate the performance for image classifications. Experimental results show that ThreshNet achieves up to 60% reduction in inference time compared to DenseNet, and up to 35% faster training speed and 20% reduction in error rate compared to HarDNet on these datasets.