Personalized Recommendation Algorithm for Interactive Medical Image Using Deep Learning

Abstract

Personalized interactive image recommendation has several issues, such as being slow or having poor recommendation quality. Therefore, we propose an image personalized recommendation algorithm (IPRA) using deep learning to improve the time and quality of personalized interactive image recommendations. First, the feature subimage is obtained and converted into a one-dimensional vector using the convolution neural network model. Single input and single output functional and dual input and single output generalized functional network model are integrated into the model to improve the learning ability of nonlinear mapping and avoid overfitting during the training process; second, a one-dimensional vector is clustered using the fuzzy k-means approach and then translated into hyperbolic space; Finally, the Poincare map model is used to map the updated vector, the transformed vector is mapped using the PM model, and the image information is fed back to the two-dimensional plane, and the image recommendation set is formed based on the ranking of similarity, and the visual recommendation is presented to the user. The results show that the size of the convolution kernel is 2 × 2, and the image one-dimensional vector clustering can be better completed. The optimal value of F1 is 0.92, and the optimal value of average time is 11 s. The image recommendation quality is better, and the image recommendation can be formed according to the photographic similarity, which has good application value.