Abstract
Detection of abnormality in Musculoskel et al. radiographs often requires the involvement of a radiologist but the recent advancement in deep learning technique helped us to detect abnormality in musculoskeletal radiographs. In this paper, each model’s performance is evaluated on unseen Mura-dataset. A novel method has been developed based on CNN architectures (DenseNet169, Vgg16 and ResNet50) by training it with MURA dataset and performed hyperparameter optimization for all the 3 models. Showing how Deep Convolution Neural Networks could be extended to medical images. DenseNet169 showed the highest accuracy. In this paper, found that model (Densenet169) achieved training accuracy of 87.88% and validation accuracy of 79.20%. In case of training loss and validation loss, the model achieved 0.3304 and 0.4887 respectively.
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I take this opportunity to thank Vellore Institute of Technology, Vellore and our Department of Communication Engineering, School of Electronics Engineering (SENSE).
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Rath, M., Reddy, P.S.D., Singh, S.K. (2022). Deep Convolutional Neural Networks (CNNs) to Detect Abnormality in Musculoskeletal Radiographs. In: Chen, J.IZ., Tavares, J.M.R.S., Iliyasu, A.M., Du, KL. (eds) Second International Conference on Image Processing and Capsule Networks. ICIPCN 2021. Lecture Notes in Networks and Systems, vol 300. Springer, Cham. https://doi.org/10.1007/978-3-030-84760-9_10
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