Knee Osteoarthritis Detection and Staging using Ordinal Classification with ResNet50

KOA is one of the most popular forms of osteoarthritis that can be diagnosed based on images we will use the Kellgren and Lawrence grading system for selfclassification. In this paper, we have explicitly available data divided into training testing and validation sets. In our approach, we are going to use Resnet50 features extraction, and the likelihood of our methods correct classifying of KOA as severe would be predicted. We wanted to extend this Approach by combining data from multiple contexts so that we can perform well across different populations. Our methods can be applied for initial detection and staging of knee osteoarthritis.