Investigation of Focal Loss in Deep Learning Models for Femur Fractures Classification

Lotfy M, Shubair RM, Navab N, Albarqouni S (2019)

Publication Type: Conference contribution

Publication year: 2019

Publisher: Institute of Electrical and Electronics Engineers Inc.

Conference Proceedings Title: 2019 International Conference on Electrical and Computing Technologies and Applications, ICECTA 2019

Event location: Ras Al Khaimah, ARE

ISBN: 9781728155326

DOI: 10.1109/ICECTA48151.2019.8959770

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.

Involved external institutions

Technische Universität München (TUM)

Germany (DE) Massachusetts Institute of Technology (MIT)

United States (USA) (US)

How to cite

APA:

Lotfy, M., Shubair, R.M., Navab, N., & Albarqouni, S. (2019). Investigation of Focal Loss in Deep Learning Models for Femur Fractures Classification. In 2019 International Conference on Electrical and Computing Technologies and Applications, ICECTA 2019. Ras Al Khaimah, ARE: Institute of Electrical and Electronics Engineers Inc..

MLA:

Lotfy, Mayar, et al. "Investigation of Focal Loss in Deep Learning Models for Femur Fractures Classification." Proceedings of the 2019 International Conference on Electrical and Computing Technologies and Applications, ICECTA 2019, Ras Al Khaimah, ARE Institute of Electrical and Electronics Engineers Inc., 2019.

BibTeX: Download