Deep Transfer Learning and Majority Voting Approaches for Osteoporosis Classification
DOI:
https://doi.org/10.18201/ijisae.2021473646Keywords:
CNN, data augmentation, osteoporosis, transfer learning, X-rayAbstract
Osteoporosis is a systemic skeletal disease characterized by low bone mass and deterioration of the micro-architectural structure of the bone tissue, increasing bone fragility, and the probability of fracture. In this study, we propose a non-invasive method for osteoporosis classification using X-ray images (plain radiographs) of the ankle. Convolutional Neural Networks along with Data Augmentation techniques and Deep Transfer Learning Architectures are combined to classify X-ray images of healthy and osteoporotic patients. The proposed approach achieved an accuracy of 99% using ResNet50, and 100% with GoogleNet.
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