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access icon free Deep learning-based automated detection of human knee joint's synovial fluid from magnetic resonance images with transfer learning

  • Author(s): Imran Iqbal 1 ; Ghazala Shahzad 2 ; Nida Rafiq 3 ; Ghulam Mustafa 4 ; Jinwen Ma 1
    • View affiliations
  • Source: Volume 14, Issue 10, 21 August 2020, p. 1990 – 1998
    DOI:  10.1049/iet-ipr.2019.1646 , Print ISSN 1751-9659, Online ISSN 1751-9667
© The Institution of Engineering and Technology
Received 03/12/2019, Accepted 12/05/2020, Revised 22/04/2020, Published 20/05/2020

As an analytic tool in medicine, particularly in radiology, deep learning is gaining much attention and opening a new way for disease diagnosis. Nonetheless, it is rather challenging to acquire large-scale detailed labelled datasets in the field of medical imaging. In fact, transfer learning provides a possible way to resolve this issue to a certain extent such that the parameter learning of a neural network starts with its pre-trained weights learned from a large-scale dataset of certain similar task, and fine-tunes on a small comprehensively annotated dataset for the particular target task. The main aim of this study is to apply the deep learning model to detect the synovial fluid of human knee joint from magnetic resonance images. A specialized convolutional neural network architecture is proposed for automated detection of human knee joint's synovial fluid. Two independent datasets are used in the training, development, and evaluation of the proposed model. It is demonstrated by the experimental results that the proposed model obtains high sensitivity, specificity, precision, and accuracy to the detection of human knee joint's synovial fluid. As a result, this proposed approach provides a novel and feasible way for automating and expediting the synovial fluid analysis.

Inspec keywords: learning (artificial intelligence); orthopaedics; neural nets; diseases; biomedical MRI

Other keywords: particular target task; deep learning-based automated detection; large-scale detailed labelled datasets; human knee joint; parameter learning; independent datasets; deep learning model; synovial fluid analysis; magnetic resonance images; medical imaging; PC Hospital Liaoning dataset; specialised convolutional neural network architecture; large-scale dataset; transfer learning; Orthopaedic Implants dataset; comprehensively annotated dataset

Subjects: Computer vision and image processing techniques; Neural computing techniques; Optical, image and video signal processing; Biomedical magnetic resonance imaging and spectroscopy; Biology and medical computing; Medical magnetic resonance imaging and spectroscopy; Patient diagnostic methods and instrumentation; Knowledge engineering techniques; Other topics in statistics

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