Version 1
: Received: 11 May 2024 / Approved: 12 May 2024 / Online: 13 May 2024 (12:15:05 CEST)
How to cite:
Hussain, D.; Hyeon Gu, Y. Exploring the Impact of Noise and Image Quality on Deep Learning Performance in Dxa Images. Preprints2024, 2024050765. https://doi.org/10.20944/preprints202405.0765.v1
Hussain, D.; Hyeon Gu, Y. Exploring the Impact of Noise and Image Quality on Deep Learning Performance in Dxa Images. Preprints 2024, 2024050765. https://doi.org/10.20944/preprints202405.0765.v1
Hussain, D.; Hyeon Gu, Y. Exploring the Impact of Noise and Image Quality on Deep Learning Performance in Dxa Images. Preprints2024, 2024050765. https://doi.org/10.20944/preprints202405.0765.v1
APA Style
Hussain, D., & Hyeon Gu, Y. (2024). Exploring the Impact of Noise and Image Quality on Deep Learning Performance in Dxa Images. Preprints. https://doi.org/10.20944/preprints202405.0765.v1
Chicago/Turabian Style
Hussain, D. and Yeong Hyeon Gu. 2024 "Exploring the Impact of Noise and Image Quality on Deep Learning Performance in Dxa Images" Preprints. https://doi.org/10.20944/preprints202405.0765.v1
Abstract
Background and Objective: Segmentation of the femur in Dual Energy X-ray (DXA) images poses challenges due to reduced contrast, noise, bone shape variations, and inconsistent X-ray beam penetration. In this study, we investigate the relationship between noise and certain deep learning (DL) techniques for semantic segmentation of the femur to enhance segmentation and bone mineral density (BMD) accuracy by incorporating noise reduction methods into DL models.
Methods: Convolutional neural network (CNN) based models were employed to segment femurs in DXA images and evaluate the effects of noise reduction filters on segmentation accuracy and its effect on BMD calculation. Various noise reduction techniques were integrated into DL-based models to enhance image quality before training. We assessed the performance of the fully convolutional neural network (FCNN) in comparison to noise reduction algorithms and manual segmentation methods.
Results: Our study demonstrated that FCNN outperformed noise reduction algorithms in enhancing segmentation accuracy and enabling precise calculation of BMD. The FCNN-based segmentation approach achieved a segmentation accuracy of 98.84% and a correlation coefficient of 0.9928 for BMD measurements, indicating its effectiveness in the clinical diagnosis of osteoporosis.
Conclusions: In conclusion, integrating noise reduction techniques into DL-based models significantly improves femur segmentation accuracy in DXA images. The FCNN model, in particular, shows promising results in enhancing BMD calculation and clinical diagnosis of osteoporosis. These findings highlight the potential of DL techniques in addressing segmentation challenges and improving diagnostic accuracy in medical imaging.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
