Conference
❝ citations
Investigation of Focal Loss in Deep Learning Models For Femur Fractures Classification
2019 International Conference on Electrical and Computing Technologies and Applications (ICECTA), 2019
Abstract
This paper develops an approach based on deep learning for the classifications of a common critical type of bone fractures, namely proximal femur. The performance of the state-of-the-art deep learning architecture, DenseNet, is investigated along with a recently introduced loss function, focal loss, to address the problem of imbalanced classes. Quantitative assessment is carried out on a real dataset consisting of 1347 X-ray images. Results demonstrate that the proposed deep learning approach utilizing focal loss show better performance for the fracture detection case and comparable results for the classification scenarios.
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Cite (BibTeX)
@inproceedings{lotfy2019investigation,
author = {Lotfy, Mayar and Shubair, Raed M and Navab, Nassir and Albarqouni, Shadi},
booktitle = {2019 International Conference on Electrical and Computing Technologies and Applications (ICECTA)},
organization = {IEEE},
pages = {1--4},
title = {Investigation of Focal Loss in Deep Learning Models For Femur Fractures Classification},
year = {2019}
}