Visualization of judgment regions in convolutional neural networks for X-ray diffraction and scattering images of aliphatic polyesters

The construction of a deep learning model and visualization of judgment regions were conducted for X-ray diffraction and scattering images of aliphatic polyesters. Due to recent progress in measurement methods, a large amount of image data can be obtained in a short time; therefore, machine learning methods are useful to determine the important regions for a given objective. Although techniques to visualize the judgment regions using deep learning have recently been developed, there have been few reports discussing whether such models can determine the important regions of X-ray diffraction and scattering images of polymeric materials. Herein, we demonstrate classification models based on convolutional neural networks (CNNs) for wide-angle X-ray diffraction and small-angle X-ray scattering images of aliphatic polyesters to predict the types of polymers and several crystallization temperatures. Furthermore, the judgment regions of the X-ray images used by the CNNs were visualized using the Grad-CAM, LIME, and SHAP methods. The main regions were diffraction and scattering peaks recognized by experts. Other areas, such as the beam centers were recognized when the intensity of the images was randomly changed. This result may contribute to developing important features in deep learning models, such as the recognition of structure-property relationships.