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

Mayar Lotfy, Raed M. Shubair, Nassir Navab, Shadi Albarqouni

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

9 Scopus citations

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.

Original languageEnglish
Title of host publication2019 International Conference on Electrical and Computing Technologies and Applications, ICECTA 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728155326
DOIs
StatePublished - Nov 2019
Event2019 International Conference on Electrical and Computing Technologies and Applications, ICECTA 2019 - Ras Al Khaimah, United Arab Emirates
Duration: 19 Nov 201921 Nov 2019

Publication series

Name2019 International Conference on Electrical and Computing Technologies and Applications, ICECTA 2019

Conference

Conference2019 International Conference on Electrical and Computing Technologies and Applications, ICECTA 2019
Country/TerritoryUnited Arab Emirates
CityRas Al Khaimah
Period19/11/1921/11/19

Keywords

  • Deep Learning
  • DenseNet
  • Focal loss
  • Proximal femur fractures

Fingerprint

Dive into the research topics of 'Investigation of Focal Loss in Deep Learning Models for Femur Fractures Classification'. Together they form a unique fingerprint.

Cite this