Deep Convolutional Neural Networks (CNNs) to Detect Abnormality in Musculoskeletal Radiographs

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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