Abstract: The continuous upgrading and development of small-angle X-ray scattering (SAXS) equipment have generated more high-dimensional scattering data, which poses great challenges for researchers to quickly obtain experimental results. Researchers urgently need effective automated classification methods to speed up data representation and obtain higher accuracy. However, many models learn features mainly for illumination images, ignoring the characteristics of scattering images and resulting in lower classification accuracy. Therefore, based on the characteristics of scattering patterns, this paper proposes a bimodal fine-grained feature extraction model called BRTNet. The model adopts a bimodal input mode. The first mode is the feature learning network PRS using a multi-scale convolution architecture, which learns the micro-information of scattering images. The second mode is the multi-head attention mechanism ConvTransformer fusing local information, which learns the correlation information of scattering sequences. Then, the model combines image information and sequence information, fuses the dual-branch features, classifies the scattering data, and obtains the classification results. Experimental results on the biological solution scattering dataset show that the model's classification accuracy exceeds 89%, which has a significant advantage over the baseline model.  

Key words: small-angle scattering image, fine-grained classification, scattering features, bimodal