TY  - JOUR
A1  - Liang, Shuang
A1  - Gu, Yu
T1  - Towards robust and accurate detection of abnormalities in musculoskeletal radiographs with a multi-network model
T2  - Sensors
N2  - 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.
KW  - CNN
KW  - GCN
KW  - abnormality detection
KW  - fusion
KW  - multi-network
KW  - musculoskeletal radiographs
Y1  - 2020
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/54894
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-548948
VL  - 20
IS  - 3153
SP  - 1
EP  - 14
PB  - MDPI
CY  - Basel
ER  -