Automated Classification of Knee Osteoarthritis Using Radiographic Analysis

Abstract: Thisprojectaimstodevelopanautomateddeeplearningmodelforclassifyingkneeosteoarthritis (KOA)severity into five stages based onthe Kellgren-Lawrence(KL)gradingsystem, usingX-ray images. KOAisadegenerativejoint diseasethataffects millionsworldwide,andaccurategrading isessential for proper diagnosis andtreatment. However,manualassessmentofXrayimagescan be subjective and time-consuming, making automation crucial for improving diagnostic efficiency and consistency. The model will utilize the ResNet-50 architecture, a powerful convolutional neural network(CNN) knownforit sability toextractcomplexfeaturesandtraindeepnetworkseffectively. ResNet-50 will process knee X-ray images and classify them into KLgrades 0 to 4, ranging from healthytosevereosteoarthritis.Byapplyingtransferlearning, themodelwillbepretrainedon large datasets and fine-tuned using KOA-specific data. Additionally, data augmentation techniques such as rotation, flipping, and zooming will be used to enhance training data diversity.The model’s performancewillbeevaluatedusingaccuracy,precision,recall,andF1-scoremetrics.Thegoalis to provide radiologists with an automated, objective tool that improves the speed and consistency of KOA diagnosis, ultimately contributing to better patient care and more efficient clinical decision- making