Title:Identification of Severe Grading in Knee OsteoArthritis from MRI using
Ensemble Deep Learning
Volume: 20
Author(s): Jaber Alyami*
Affiliation:
- Department of Radiologic Sciences, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah 21589, Saudi Arabia
- Animal House Unit, King Fahd Medical Research Center, King Abdulaziz University, Jeddah 21589, Saudi Arabia
- Smart Medical Imaging Research Group, King Abdulaziz University, Jeddah 21589, Saudi Arabia
Keywords:
Knee osteoarthritis, MRI, DL models, Health risks, Osteoarthritis Initiative (OAI) dataset, Deep learning.
Abstract:
Background:
Accurate finding of Knee Osteoarthritis (KOA) from structural Magnetic Resonance Imaging (MRI) is a difficult task and is greatly subject to user
variation. Furthermore, the identification of knee osteoarthritis (KOA) from MRI scans presents a challenge due to the limited information
available. A novel methodology using an ensemble Deep Learning algorithm, combining EfficientNet-B3 and ResNext-101 architectures, aims to
forecast KOA advancement, bridging the identified gap in clinical trials.
Objectives:
The study aims to develop a precise predictive model for knee osteoarthritis using advanced deep-learning architectures and structural MRI scan
data. By utilizing an ensemble technique, the model's accuracy in predicting disease development is enhanced, surpassing the limitations of
traditional biomarkers.
Methods:
The study used the Osteoarthritis Initiative dataset to develop an ensemble Deep Learning model that combined EfficientNet-B3 and ResNext-101
architectures. Techniques like cropping, gamma correction, and in-slice rotation were used to expand the dataset and improve the model's
generalization capacity.
Results:
The Deep Learning model demonstrated 93% validation accuracy on the OAI dataset, accurately capturing subtle patterns of knee osteoarthritis
progression. Augmentation approaches enhanced its resilience.
Conclusion:
Our ensemble Deep Learning approach, using ResNext-101 and EfficientNet-B3 architectures, accurately predicts knee osteoarthritis courses using
structural MRI data, demonstrating the importance of data augmentation for improved predictive tools.
