A Comparative Analysis of Deep Learning Models for Detection of Knee Osteoarthritis Disease through Mobile Apps

Osteoarthritis is a progressive degenerative joint disease characterized by damage to joint cartilage and structural joint diarthrodial. As many as 80% of osteoarthritis patients will experience movement limitations, and 25% cannot carry out daily activities. At present, the stages of osteoarthritis diagnosis are through visual observations of the narrowing of joint and osteophyte gaps contained in X-ray images through the observation of radiology and assessing the severity of disorders. This study proposes a CAD (Computer-Aided Design) support system to detect knee osteoarthritis based on X-ray images with deep learning (DL) techniques. In the design of the CAD-related architecture evaluation, including ResNet, VggNet, EfficientNet, and InceptionV3 in building a predictive model using the Convolutional Neural Network (CNN) based on Mobile Application and Cloud Computing to be able to classify normal knee and knee osteoarthritis with the percentage of testing accuracy each architecture is 93.17% on ResNet, 95.26% on VggNet, 94.64% in InceptionV3 and 96.59% on EfficientNet. The experimental results can be found through our project channel: https://youtu.be/EXj-7Dv6xGM