Medical image retrieval based on the deep convolution network and hash coding

Recent years CNN (Convolutional Neural Network) has performed well in image processing, including image retrieval. However, since the features of CNN extraction are usually high-dimensional, and in the massive data conditions, it is a rather time-consuming process to traverse all the images and calculate the distance between the feature vectors to accurately find the closest Top K images. The proposed paper uses an effective deep learning framework in which Deep Convolution Network is combined with Hash Coding to learn rich medical image representing through CNN. First, a hash layer is added to the network to represent the image information as binary hashing codes; Simultaneously, the dimension of feature vector is effectively reduced by the framework; then, In order to improve the accuracy of image retrieval, rough searching and fine searching are combined. The experimental results show that our method is optimal than several hashing algorithms and CNN methods on the TCIA-CT database and VIA/I-ELCAP database.