scholarly journals Annotation of enhanced radiographs for medical image retrieval with deep convolutional neural networks

PLoS ONE ◽  
2018 ◽  
Vol 13 (11) ◽  
pp. e0206229 ◽  
Author(s):  
Obioma Pelka ◽  
Felix Nensa ◽  
Christoph M. Friedrich
Keyword(s):  
Neural Networks ◽  
Image Retrieval ◽  
Convolutional Neural Networks ◽  
Medical Image ◽  
Deep Convolutional Neural Networks ◽  
Medical Image Retrieval

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.

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