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Dental caries is a bacterial infectious disease that destroys the structure of teeth. It is one of the main diseases that endanger human health [R. H. Selwitz, A. I. Ismail, and N. B. Pitts, Lancet 369(9555), 51–59 (2007)]. At present, dentists use both visual exams and radiographs for the detection of caries. Affected by the patient's dental health and the degree of caries demineralization, it is sometimes difficult to accurately identify some dental caries in x-ray images with the naked eye. Therefore, dentists need an intelligent and accurate dental caries recognition system to assist diagnosis, reduce the influence of doctors' subjective factors, and improve the efficiency of dental caries diagnosis. Therefore, this paper combines the U-Net model verified in the field of biomedical image segmentation with the convolution block attention module, designs an Attention U-Net model for caries image segmentation, and discusses the feasibility of deep learning technology in caries image recognition so as to prepare for the next clinical verification. After testing, the Dice similarity coefficient, mean pixel accuracy, mean intersection over union, and frequency-weighted intersection over the union of teeth segmentation with Attention U-Net are 95.30%, 94.46%, 93.10%, and 93.54%, respectively. The Dice similarity coefficient, mean pixel accuracy, mean intersection over union, and frequency-weighted intersection over the union of dental caries segmentation with Attention U-Net are 85.36%, 91.84%, 82.22%, and 97.08%, respectively. As a proof of concept study, this study was an initial evaluation of technology to assist dentists in the detection of caries. There is still more work needed before this can be used clinically.

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In the intelligent production process of wood products, the classification system of wood knot defects is a very practical solution. However, traditional image processing methods cannot handle it well due to the uncertainty of manually extracted features. Therefore, a lightweight and reliable artificial neural network model is proposed to classify and identify our objective. To solve this problem, a wood knot defect recognition model named SE-ResNet18 combining convolutional neural network, attention mechanism, and transfer learning is proposed in this paper. First, the Sequence-and-Exception (SE) module is combined with Basicblock and is constructed as two modules called RBBSE-1 and RBBSE-2. These modules learn to enhance features that are useful for the current task, suppress useless features, and fuse the output features with the original features. Then, the fully connected layer is replaced with a global average pooling layer, which can effectively reduce the parameters of the fully connected layer in the model. Finally, a SE-ResNet18 was constructed by one convolutional layer, five RBBSE-1 modules, and three RBBSE-2 modules of different channels. The SE-ResNet18 has a higher accuracy (98.85%) in the test set compared to the unimproved model ResNet-18. Compared with the previously proposed ReSENet-18, more SE modules are used in SE-ResNet18 to provide a basis for future training on a larger-scale dataset. Based on the same test set, a comparison with other classical models (such as LeNet-5, AlexNet, etc.) was conducted, and the results validated the superiority of the proposed model. The proposed model achieves the expected objective and provides a new way of thinking for non-destructive testing of wood.