An Attention-Based Deep Learning Approach to Knee Injury Classification from MRI Images

Knee injuries, prevalent in athletic and aging populations, pose significant challenges to healthcare professionals due to their complex nature and the critical function of the knee joint. Early and accurate diagnosis is paramount to ensure effective treatment and minimize long-term complications. Traditional diagnostic methods, including physical examinations and imaging techniques like MRI, require expert interpretation and can sometimes be inconclusive. This study introduces an approach to knee injury classification using deep learning techniques by leveraging convolutional neural networks (CNNs) with Attention Mechanism. This research work integrates powerful feature extraction capabilities of CNN and feature refinement of attention mechanism for the binary and multi-class classification of knee MRI images, with the aim of accurately identifying specific knee injury types. Based on our experiment on two comprehensive knee MRI datasets, our custom CNN model achieved 88% testing accuracy on Dataset-1 (Binary classification) and 77% accuracy on Dataset-2 (Multi-class classification). Meanwhile, the Attention-based CNN model achieved 100% accuracy on Dataset- 1 (Binary Classification) and 91% accuracy on Dataset-2 (MultiClass Classification). This approach not only holds promise for enhancing diagnostic accuracy but also for reducing the time to diagnosis.