  This research paper explores the classification of knee osteoarthritis (OA)
severity levels using advanced computer vision models and augmentation
techniques. The study investigates the effectiveness of data preprocessing,
including Contrast-Limited Adaptive Histogram Equalization (CLAHE), and data
augmentation using diffusion models. Three experiments were conducted: training
models on the original dataset, training models on the preprocessed dataset,
and training models on the augmented dataset. The results show that data
preprocessing and augmentation significantly improve the accuracy of the
models. The EfficientNetB3 model achieved the highest accuracy of 84\% on the
augmented dataset. Additionally, attention visualization techniques, such as
Grad-CAM, are utilized to provide detailed attention maps, enhancing the
understanding and trustworthiness of the models. These findings highlight the
potential of combining advanced models with augmented data and attention
visualization for accurate knee OA severity classification.

Computer Vision and Pattern Recognition;Artificial Intelligence;Machine Learning