An Autonomous System for Knee Osteoarthritis Disease Diagnosis using Machine Learning and Standalone Controller

Abstract

Due to the recent pandemic, health-related awareness has increased among civilians. Not only this, but from advancements in mobile devices, Health-related mobile applications for disease diagnosis boomed in recent years. The majority of applications diagnose simple decease like colds, fever, headaches, etc., and schedule online doctor’s appointments. However, there has been very limited or no support for severe decease like cancer and orthopedic diagnosis. This paper proposes an autonomous disease diagnosis system for knee OA (osteoarthritis) using machine learning methods. The methods for predicting the severity of knee OA are ResNetv2 (Residual Networks 2) and VGG-19 (Visual Geometry Group-19) models are available with prediction accuracy of 64% and 41%, respectively, which is very poor for medical applications. Therefore new method has been proposed using Enhanced VGG-19, which is used to predict the severity of disease and has improved prediction accuracy by up to 97%. After optimizing the stand-alone model, a system is developed where the user has to send a mail or upload an X-Ray image of a particular body part to a specific email ID. The server/system will automatically diagnose for selected disease and generates a report based on that. The server has various optimized trained models for different decease, which will reduce human factors for stakeholders. By using these reports, doctors can save time and the doctor can utilize their time for consulting more patients.