Automated KL grading of knee X-ray images using convolutional neural network Online publication date: Wed, 05-Jun-2024
by S. Rajkumar; V.A. Sairam; R. Saranya; N. Sandhiya; V. Shivanie; V. Sapthagirivasan
International Journal of Biomedical Engineering and Technology (IJBET), Vol. 45, No. 3, 2024
Abstract: Knee osteoarthritis affects people across the globe; Kellegren Lawrence's grading method is widely used for diagnosing and grading the diseased condition based on X-ray images. The work aims to develop an AI tool trained by deep learning (DL) algorithms to perform automated classification of the grades of deterioration using knee X-ray image. A modified version of Inception-ResNet-v2, which uses transfer learning, is developed as a CNN model to classify the KL grade of knee X-ray images. Open source OAI dataset containing X-ray images used 9,786 images with ground truth labelling. A web-based AI tool is developed to categorise knee X-ray images into one of five KL grades. The classifier developed and trained on the OAI dataset (curated) produced 75% validation accuracy, 0.74 validation loss, 78% specificity, 69% sensitivity, and 0.942 AUC. The proposed model is helpful to clinical professionals to know the knee osteoarthritis condition and improves diagnostic quality.
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