Aiming at the insufficiency of feature extraction in the existing Content-Based Medical Image Retrieval (CBMIR) algorithms, which resulted in imperfect semantic information representation and poor image retrieval performance, an algorithm based on multi-scale dense network was proposed. Firstly, the size of pulmonary nodule image was reduced from 512×512 to 64×64, and the dense block was added to solve the gap between the extracted low-level features and high-level semantic features. Secondly, as the information of pulmonary nodule images extracted by different layers in the network was varied, in order to improve the retrieval accuracy and efficiency, the multi-scale method was used to combine the global features of the image and the local features of the nodules, so as to generate the retrieval hash code. Finally, the experimental results show that compared with the Adaptive Bit Retrieval (ABR) algorithm, the average retrieval accuracy for pulmonary nodule images based on the proposed algorithm under 64-bit hash code length can reach 91.17%, which is increased by 3.5 percentage points; and the average time required to retrieve a lung slice is 48 μs. The retrieval results of the proposed algorithm are superior to other comparative network structures in expressing rich semantic features and retrieval efficiency of images. The proposed algorithm can contribute to doctor diagnosis and patient treament.

Aiming at the problem of small gradient, low learning rate, slow convergence of error during the process of using Deep Belief Network (DBN) algorithm to correct connection weight and bias of network by the method of back propagation, a new algorithm called Multi-Innovation DBN (MI-DBN) was proposed based on combination of standard DBN algorithm with multi-innovation theory. The back propagation process in standard DBN algorithm was remodeled to make full use of multiple innovations in previous cycles, while the original algorithm can only use single innovation. Thus, the convergence rate of error was significantly increased. MI-DBN algorithm and other representative classifiers were compared through experiments of datasets classification. Experimental results show that MI-DBN algorithm has a faster convergence rate than other sorting algorithms; especially when identifying MNIST and Caltech101 dataset, MI-DBN algorithm has the fewest inaccuracies among all the algorithms.