Deep learning‐based automated detection of human knee joint's synovial fluid from magnetic resonance images with transfer learning. Issue 10 (15th July 2020)

• Record Type: Journal Article
• Authors: Iqbal, Imran; Shahzad, Ghazala; Rafiq, Nida; Mustafa, Ghulam; Ma, Jinwen
• Is Part Of: IET image processing. Volume 14:Issue 10(2020)
• Page Start: 1990
• Page End: 1998
• Publication Date: 2020-07-15
• Subjects: learning (artificial intelligence) -- neural nets -- diseases -- orthopaedics -- biomedical MRI; magnetic resonance images -- specialised convolutional neural network architecture -- human knee joint -- independent datasets -- Orthopaedic Implants dataset -- PC Hospital Liaoning dataset -- synovial fluid analysis -- deep learning‐based automated detection -- transfer learning -- large‐scale detailed labelled datasets -- medical imaging -- parameter learning -- large‐scale dataset -- comprehensively annotated dataset -- particular target task -- deep learning model; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2019.1646
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : 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.