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With the proliferation of digital imaging data in hospitals, the amount of medical images is increasing rapidly. Thus, the need for efficient retrieval systems, to find relevant information from large medical datasets, becomes high. The Convolutional Neural Network (CNN)-based models have been proved to be effective in several areas including, for example, medical image retrieval. Moreover, the Text-Based Image Retrieval (TBIR) was successful in retrieving images with textual description. However, in TBIR, all queries and documents are processed without taking into account the influence of certain medical terminologies (Specific Medical Features (SMF)) on the retrieval performance. In this paper, we propose a re-ranking method using the CNN and the SMF for text-medical image retrieval. First, images (documents) and queries are indexed to specific medical image features. Second, the Word2vec tool is used to construct feature vectors for both documents and queries. These vectors are then integrated into a neural network process and a matching function is used to re-rank documents obtained initially by a classical retrieval model. To evaluate our approach, several experiments are carried out with Medical ImageCLEF datasets from 2009 to 2012. Results show that our proposed approach significantly enhances image retrieval performance compared to several state of the art models. |
