Corrigendum to: Deep Learning-based Automated Knee Joint Localization in Radiographic Images Using Faster R-CNN

Area of Science:

• Medical Imaging
• Artificial Intelligence
• Computer Science

Background:

• Accurate localization of knee joints in radiographic images is crucial for diagnosis and treatment planning.
• Automated methods can improve efficiency and consistency in medical image analysis.

Purpose of the Study:

• To present an updated author affiliation for the study titled 'Deep Learning-based Automated Knee Joint Localization in Radiographic Images Using Faster R-CNN'.
• To ensure accurate attribution for research in automated medical image analysis.

Main Methods:

• The study employed a deep learning approach, specifically the Faster R-CNN model.
• Radiographic images of knee joints were utilized for the localization task.

Main Results:

• The Faster R-CNN model demonstrated effectiveness in automated knee joint localization.
• The updated affiliation reflects the researchers' institutional details.

Conclusions:

• The research highlights the potential of deep learning for enhancing knee joint localization in radiographic imaging.
• Accurate author affiliations are essential for scientific integrity and collaboration.