A Hybrid Approach of Semantic Weight Based Re-Propagation For Convolutional Neural Networks in Content Based Medical Image Retrieval

Abstract

Content-based image retrieval (CBIR) is a homogeneous search technology based on visual features, and information has been leaked to potential users of pharmaceutical testing, education, and research. However, the CBIR collects images related to acquisition properties, as a result of the inability to predict the similarity of images using the medical part, and the difference between the user’s search destination and object space. In the existing methods, effective points of interest are searched for, and L2 normalization is applied to the spatial arrangement of the original and global features and the RGB channels in the index image by effectively detecting their points of interest. The principal component analysis is used for redundant feature sets and feature sets to provide significant effects. The existing system has certain limitations, such as information loss during the hard task completion coding process and ignores the spatial relationship among the patches in image representation. To overcome the existing method issues, the integrated feature of the images is preferred. In this work, Semantic Weight Based Feed Forward Recurrent Back Propagation (SWFFRBP) is used to retrieve the images, and then based on the semantic weight the correctness of retrieval of images is calculated. The semantic weight determines the accuracy of retrieved images. After that, the recurrent backpropagation calculates the weight error derivatives arising from this entire episode of processing in the network. The existing features compared with the proposed features were Bag of Features (BoF), SURF, and HOG. The retrieval methods taken to compare with proposed retrieval techniques are RFRM and KNN. Thus, medical image retrieval for diagnosis using SWFFRBP, accuracy, False positive rate, relevance, feedback, precision, and recall parameters were tested and proved to be efficient than other methods.