Open AccessAn Insight Framework for Content based Medical Image Retrieval using Deep Convolutional Neural Network
Author(s) -
P Haripriya,
R Porkodi
Publication year - 2019
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.a1043.1091s19
Subject(s) - dicom , computer science , image retrieval , convolutional neural network , information retrieval , feature extraction , precision and recall , feature (linguistics) , artificial intelligence , image (mathematics) , computer vision , linguistics , philosophy
Content Based Image Retrieval system is essentially proven techniques for retrieving the DICOM based images. DICOM format is use to acquire, store and shared medical images. Typically, retrieval of image under query upon the medical image databases is performed by fusing the low-level and high-level descriptors along with DCNN features. In this paper, DICOM image meta data are extracted by using dicom function and create the local database for reserve the information. To accomplish the initial search and retrieve the images by using extraction of Semantic information. The DICOM tags are extracted from the DICOM images and use DCNN feature to build a feature vector database. Subsequently prediction is done based on the by leveraging the convolution layers based on the meta data along with DCNN image features. This paper attempts to implement pre filter method to all DICOM images which further decrease the searching no of images, searching time and ultimately gives fast processing. DCNN based prediction model was constructed and finest results are accomplished. Average accuracy of precision and recall up to0.80 and 0.87 respectively is achieved based on precision value which would be suitable for high quality image retrieval based on semantic information confined in the image.