Title: Automatic knee anterior cruciate ligament torn diagnosis using CNN-XGBoost

Authors: Kamel H. Rahouma; Ahmed Salama Abdel Latif; Kadry Ali Ezzat

Addresses: University of Minia, Minia, Egypt ' Egyptain Space Agency, Cairo, Egypt ' Biomedical Engineering Department, Higher Technological Institute, 10th of Ramadan City, Egypt

Abstract: The knee joint is very important for everyone as it helps us in movements, which is essential for everyone. One of the most diseases that injure the knee is the anterior cruciate ligament (ACL). This work has developed a computer aided diagnosis (CAD) system for examining the given knee magnetic resonance imaging (MRI) image and automatically determining if there is a torn in ACL or not. The region growing based segmentation algorithm is used to get the region of interest (ROI) from MRI image, e.g., extract ACL region from knee image then CNN-XGBoost model is used for knee ACL classification. The model is divided into two main parts: the first part extract the feature uses CNN and the second part using XGBoost for feature classification. The designed model gives us an accuracy of 91%.

Keywords: deep learning; CNN; XGBoost; knee ACL.

DOI: 10.1504/IJMEI.2025.143278

International Journal of Medical Engineering and Informatics, 2025 Vol.17 No.1, pp.1 - 12

Received: 25 Feb 2022
Accepted: 13 Jul 2022
Published online: 12 Dec 2024 *

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