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Title: Deep Transfer Learning and Majority Voting Approaches for Osteoporosis Classification
Authors: Ashames, M.M.
Ceylan, Murat
Jennane, R.
Keywords: CNN
Data augmentation
Transfer learning
Issue Date: 2021
Publisher: Ismail Saritas
Abstract: Osteoporosis is a systemic skeletal disease characterized by low bone mass density 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. © 2021, Ismail Saritas. All rights reserved.
ISSN: 2147-6799
Appears in Collections:Mühendislik ve Doğa Bilimleri Fakültesi Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections

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