Literature DB >> 33249389

A deep community based approach for large scale content based X-ray image retrieval.

Nandinee Fariah Haq1, Mehdi Moradi2, Z Jane Wang3.   

Abstract

A computer assisted system for automatic retrieval of medical images with similar image contents can serve as an efficient management tool for handling and mining large scale data, and can also be used as a tool in clinical decision support systems. In this paper, we propose a deep community based automated medical image retrieval framework for extracting similar images from a large scale X-ray database. The framework integrates a deep learning-based image feature generation approach and a network community detection technique to extract similar images. When compared with the state-of-the-art medical image retrieval techniques, the proposed approach demonstrated improved performance. We evaluated the performance of the proposed method on two large scale chest X-ray datasets, where given a query image, the proposed approach was able to extract images with similar disease labels with a precision of 85%. To the best of our knowledge, this is the first deep community based image retrieval application on large scale chest X-ray database.
Copyright © 2020 Elsevier B.V. All rights reserved.

Keywords:  Community detection; Content based image retrieval (CBIR); Deep learning; Graph

Mesh:

Year:  2020        PMID: 33249389     DOI: 10.1016/j.media.2020.101847

Source DB:  PubMed          Journal:  Med Image Anal        ISSN: 1361-8415            Impact factor:   8.545


  1 in total

1.  Disease Localization and Severity Assessment in Chest X-Ray Images using Multi-Stage Superpixels Classification.

Authors:  Tej Bahadur Chandra; Bikesh Kumar Singh; Deepak Jain
Journal:  Comput Methods Programs Biomed       Date:  2022-06-09       Impact factor: 7.027

  1 in total

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