Towards robust and accurate detection of abnormalities in musculoskeletal radiographs with a multi-network model

  • This study proposes a novel multi-network architecture consisting of a multi-scale convolution neural network (MSCNN) with fully connected graph convolution network (GCN), named MSCNN-GCN, for the detection of musculoskeletal abnormalities via musculoskeletal radiographs. To obtain both detailed and contextual information for a better description of the characteristics of the radiographs, the designed MSCNN contains three subnetwork sequences (three different scales). It maintains high resolution in each sub-network, while fusing features with different resolutions. A GCN structure was employed to demonstrate global structure information of the images. Furthermore, both the outputs of MSCNN and GCN were fused through the concat of the two feature vectors from them, thus making the novel framework more discriminative. The effectiveness of this model was verified by comparing the performance of radiologists and three popular CNN models (DenseNet169, CapsNet, and MSCNN) with three evaluation metrics (Accuracy, F1 score, and Kappa score) using the MURA dataset (a large dataset of bone X-rays). Experimental results showed that the proposed framework not only reached the highest accuracy, but also demonstrated top scores on both F1 metric and kappa metric. This indicates that the proposed model achieves high accuracy and strong robustness in musculoskeletal radiographs, which presents strong potential for a feasible scheme with intelligent medical cases.

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Author:Shuang LiangORCiD, Yu GuORCiD
Pubmed Id:
Parent Title (English):Sensors
Place of publication:Basel
Document Type:Article
Date of Publication (online):2020/06/02
Date of first Publication:2020/06/02
Publishing Institution:Universit├Ątsbibliothek Johann Christian Senckenberg
Release Date:2020/06/11
Tag:CNN; GCN; abnormality detection; fusion; multi-network; musculoskeletal radiographs
Page Number:14
First Page:1
Last Page:14
Institutes:Biochemie, Chemie und Pharmazie / Biochemie und Chemie
Dewey Decimal Classification:5 Naturwissenschaften und Mathematik / 54 Chemie / 540 Chemie und zugeordnete Wissenschaften
6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit / 610 Medizin und Gesundheit
Licence (German):License LogoCreative Commons - Namensnennung 4.0