An Ensemble Deep Learning Approach for Automated Knee Osteoarthritis Detection Using X-Ray Images

Osteoarthritis of the knee is a common joint disorder where the cartilage in the knee gradually deteriorates over time. It often causes stiffness, reduced movement, and persistent discomfort, especially in older persons. For better treatment and to stop more joint damage, early identification of KOA and precise evaluation of its severity are essential. In traditional diagnosis, knee X-ray pictures are mostly evaluated visually using the Kellgren-Lawrence (KL) grading system. Hand-reading X-ray pictures takes time and may differ depending on the doctor's experience and preferences. To address these issues, this work proposes an automated deep learning-based method for the detection and classification of knee osteoarthritis from radiography pictures. The suggested method uses transfer learning with two potent convolutional neural network models, InceptionV3 and NASNetLarge, to extract comprehensive information from knee X-ray images. Before starting the model's training, some image processing tasks are finished, including resizing, normalising, and improving the photos. These processes help to enhance the quality of the data and make it possible for the model to perform well on a range of data sources. The program uses the KL grading scale to categorise knee photos into various seriousness levels. Additionally, a group technique is used to aggregate the predictions from both models, improving overall performance and raising the dependability of the classification.