Detecting Early Stage Knee Osteoarthritis Using Deep Transfer Learning

Knee osteoarthritis is one of the most prevalent forms of the disease, and its diagnosis can be challenging, especially in its early stages. Imaging techniques such as X-Ray are commonly used to diagnose osteoarthritis, but the interpretation of these images can be subjective and prone to error, especially when detecting subtle changes. In this research, we aim to develop a deep learning network that can classify Knee X-ray images into 5 categories based on the presence and severity of osteoarthritis. We propose to use Convolutional Neural Networks (CNN) for a multi-class image classification. Our baseline model will be a CNN-based deep learning network, which will be trained on a dataset of knee X-ray images. Additionally, we will investigate the effectiveness of transfer learning by applying state-of-the-art CNN architectures such as ResNet, VGG, and Vision Transformers to the classification task. Transformers are a type of neural network architecture that have been highly effective in natural language processing tasks, and we want to explore their potential for image recognition tasks. Vision Transformers are relatively new in the field of computer vision. Unlike traditional CNNs, which rely on a hierarchical feature extraction process, Vision Transformers use self-attention mechanisms to capture global and local relationships between image features. While they have shown promising results in natural language processing tasks, we aim to investigate their potential for image classification tasks