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Dergiler / 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
Sayfa
256–265
Yayın Tarihi
2022-10-18
DOI
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Yapay Zeka Hızlı Bakış & Çıkarımlar

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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

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

📌 3 Maddede Bu Makale

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Amaç & Hipotez: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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Temel Bulgular & Veri: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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Sonuç & Katkı:The proposed approach achieved an accuracy of 99% using ResNet50, and 100% with GoogleNet.
🔬Tür:Araştırma Makalesi
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Alan Parametreleri

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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Bu Makaleye Atıf Yap

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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Kaynakça (4)

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