scholarly journals An Enhanced Framework for Content Based Medical Image Retrieval using Deep Neural Network

2019 ◽  
Vol 8 (5S3) ◽  
pp. 239-244
Keyword(s):  
Image Retrieval ◽  
Medical Image ◽  
Low Cost ◽  
Image Features ◽  
Semantic Gap ◽  
Medical Image Retrieval ◽  
Large Size ◽  
Image Repository ◽  
High Level ◽  
Technology Growth

As the technology growth fuelled by low cost tech in the areas of compute, storage the need for faster retrieval and processing of data is becoming paramount for organizations. The medical domain predominantly for medical image processing with large size is critical for making life critical decisions. Healthcare community relies upon technologies for faster and accurate retrieval of images. Traditional, existing problem of efficient and similar medical image retrieval from huge image repository are reduced by Content Based Image Retrieval (CBIR) . The major challenging is an semantic gap in CBIR system among low and high level image features. This paper proposed, enhanced framework for content based medical image retrieval using DNN to overcome the semantic gap problem. It is outlines the steps which can be leveraged to search the historic medical image repository with the help of image features to retrieve closely relevant historic image for faster decision making from huge volume of database. The proposed system is assessed by inquisitive amount of images and the performance efficiency is calculated by precision and recall evaluation metrics. Experimental results obtained the retrieval accuracy is 79% based on precision and recall and this approach is preformed very effectively for image retrieval performance.

scholarly journals Convolutional neural networks and hash learning for feature extraction and of fast retrieval of pulmonary nodules

2018 ◽  
Vol 15 (3) ◽  
pp. 517-531 ◽  
Author(s):  
Pinle Qin ◽  
Jun Chen ◽  
Kai Zhang ◽  
Rui Chai
Keyword(s):  
Image Retrieval ◽  
Visual Information ◽  
Medical Image ◽  
Medical Knowledge ◽  
Pulmonary Nodules ◽  
Semantic Features ◽  
Imaging Data ◽  
Medical Image Retrieval ◽  
Segmented Images ◽  
High Level

With a widespread use of digital imaging data in hospitals, the size of medical image repositories is increasing rapidly. This causes difficulty in managing and querying these large databases leading to the need of content based medical image retrieval (CBMIR) systems. A major challenge in CBMIR systems is the ?semantic gap? that exists between the low level visual information captured by imaging devices and high level semantic information perceived by the human. Using deep convolution neural network (CNN) to construct the CBMIR system can fully characterize the high level semantic features information for medical image retrieval. The existing network mostly used for the natural images can?t produce a good result directly applied to medical image. This paper used UNet method to preprocessing under the guidance of medical knowledge. Then, multi-scale receiving field convolution module is used to extract features of the segmented images with different sizes. Finally, encoded the features and used a coarse to fine search strategy with an average search accuracy of 0.73.

scholarly journals Convolutional Neural Networks in Image Retrieval System

2020 ◽  
Vol 9 (5) ◽  
pp. 123-129
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Image Retrieval ◽  
Convolutional Neural Networks ◽  
Medical Image ◽  
Retrieval System ◽  
Prediction Models ◽  
Image Features ◽  
Medical Image Retrieval ◽  
Image Retrieval System

Image is an important medium for monitoring the treatment responses of patient’s diseases by the physicians. There could be a tough task to organize and retrieve images in structured manner with respect to incredible increase of images in Hospitals. Text based image retrieval may prone to human error and may have large deviation across different images. Content-Based Medical Image Retrieval(CBMIR) system plays a major role to retrieve the required images from the huge database.Recent advances in Deep Learning (DL) have made greater achievements for solving complex problems in computer vision ,graphics and image processing. The deep architecture of Convolutional Neural Networks (CNN) can combine the low-level features into high-level features which could learn the semantic representation from images. Deep learning can help to extract, select and classify image features, measure the predictive target and gives prediction models to assist physician efficiently. The motivation of this paper is to provide the analysis of medical image retrieval system using CNN algorithm.

A Multi-Label Image Annotation With Multi-Level Tagging System

2018 ◽  
pp. 28-47
Author(s):  
Kalaivani Anbarasan ◽  
Chitrakala S.
Keyword(s):  
Image Retrieval ◽  
Retrieval System ◽  
Image Annotation ◽  
Image Features ◽  
Semantic Gap ◽  
Low Level ◽  
Image Retrieval System ◽  
Multi Level ◽  
High Level ◽  
Tagging System

The content based image retrieval system retrieves relevant images based on image features. The lack of performance in the content based image retrieval system is due to the semantic gap. Image annotation is a solution to bridge the semantic gap between low-level content features and high-level semantic concepts Image annotation is defined as tagging images with a single or multiple keywords based on low-level image features. The major issue in building an effective annotation framework is the integration of both low level visual features and high-level textual information into an annotation model. This chapter focus on new statistical-based image annotation model towards semantic based image retrieval system. A multi-label image annotation with multi-level tagging system is introduced to annotate image regions with class labels and extract color, location and topological tags of segmented image regions. The proposed method produced encouraging results and the experimental results outperformed state-of-the-art methods

scholarly journals An Insight Framework for Content based Medical Image Retrieval using Deep Convolutional Neural Network

2019 ◽  
Vol 9 (1S) ◽  
pp. 172-179
Keyword(s):  
Image Retrieval ◽  
Medical Image ◽  
Semantic Information ◽  
Image Features ◽  
Filter Method ◽  
Meta Data ◽  
Average Accuracy ◽  
Fast Processing ◽  
Image Retrieval System ◽  
High Level

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.

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