Deep Recurrent Neural Network with Tanimoto Similarity and MKSIFT Features for Medical Image Search and Retrieval

Abstract

The innovation of digital medical images has led to the requirement of rich descriptors and efficient retrieval tool. Thus, the Content Based Image Retrieval (CBIR) technique is essential in the domain of image retrieval. Due to the growing medical image data, the searching or retrieving a relevant image from the dataset is a major problem. To address this problem, this paper propose a new medical image retrieval technique, namely Multiple Kernel Scale Invariant Feature Transform-based Deep Recurrent Neural Network (MKSIFT-Deep RNN) using the image contents. The goal is to present an effective tool that can be utilized for effective retrieval of image from huge medical image database. Here, MKSIFT is adapted for extracting the relevant features obtained from acquired input image. Moreover, MKSIFT evaluates the key point descriptor using kernels functions, wherein the weights are allocated to kernels. The feature vectors are employed in the Deep RNN for classifying the images by training the classifier, which is considered as training phase. In testing phase, a set of query images is given to the classifier which adapts Tanimoto similarity for retrieving the images. The proposed MKSIFT-Deep RNN outperformed other methods with maximal precision of 93.723%, maximal recall of 93.652% and maximal F-measure of 93.687%.