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Journals / International Journal of Intelligent Systems and Applications in Engineering / 2021 / Cilt: 9 - Sayı: 4

Deep Transfer Learning and Majority Voting Approaches for Osteoporosis Classification

Murat Ceylan
Mohamad Melad ASHAMES
Rachid Jennane
Pages
256–265
Publication Date
2022-10-18
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The proposed approach achieved an accuracy of 99% using ResNet50, and 100% with GoogleNet.

📌 3 Key Takeaways

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Objective & Hypothesis: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.
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Key Findings & Data:In this study, we propose a non-invasive method for osteoporosis classification using X-ray images (plain radiographs) of the ankle.
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Conclusion & Impact:The proposed approach achieved an accuracy of 99% using ResNet50, and 100% with GoogleNet.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

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.

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Cite This Article

Ceylan, M., ASHAMES, M. M., Jennane, R. (2022). Deep Transfer Learning and Majority Voting Approaches for Osteoporosis Classification. International Journal of Intelligent Systems and Applications in Engineering, Cilt: 9 - Sayı: 4, 256–265. https://ajindex.com/articles/541284

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References (4)

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