Leveraging Machine Learning and Deep Learning Techniques to Identify Deformation in Knee for Assisting Replacement Surgery
Keywords:
comparative, replacement, VGG16, DeformationAbstract
The rise in knee injury has increased and hence the need for advanced technology is needed to reduce the duration of recovery required to cure the knee replacement. This work first introduces the knee replacement issue and then tries to review the work that has been carried out in this regard. This paper also tries to produce the detection for knee replacement with more accuracy using the science of Deep Learning which is extended version of Machine Learning. This work focuses on detection of Deformation in Knee using three methods i.e. Convolutional Neural Network, Transfer Learning and the proposed method based on enhancement of VGG16. A comparative analysis is made based on performance metrics that shows the proposed model outperforms the rest two in terms of these metrics. The proposed method achieves an accuracy of 94.5%, surpassing CNN’s 91.2% and Transfer learning 92%.
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