Implementation of medical image retrieval algorithm based on preference learning model

This paper utilizes the Stacked Denoising Auto Encoder (SDAE) model to capture the general edge features of images. Additionally, it employs the Convolutional Neural Networks model to extract fine edge features from medical images. By combining preference learning algorithms, the paper implements an image retrieval system and designs a medical image retrieval model termed SDAE+CNN+Preference Learning. In the design of the reconstruction residual function in the SDAE model, noise factors are introduced to ensure the accuracy of image feature extraction by accounting for the impact of noise. Adjustments have been made to the structure of the CNN model, specifically in the convolutional layers, pooling layers, and fully connected layers. These adjustments aim to further enhance the accuracy of image feature extraction and ensure the precision of image feature classification. By conducting learning training on key parameters, an optimal preference learning model is obtained. Following the rules for matching the category attributes of input images with those in the medical image database and optimizing the loss function of the preference learning model, the similarity of category attributes for images is ranked. Ultimately, images with higher category attribute similarity are listed as retrieval results. Relevant experiments indicate that the proposed SDAE+CNN image feature extraction module in this paper achieves high accuracy in extracting image features, effectively controlling the influence of image noise; The designed SDAE+CNN+Preference Learning medical image retrieval model achieves a retrieval similarity of 96%, a retrieval accuracy of 94%, and a retrieval recall rate of 95%, as per the conducted experiments.