Knee MRI image Injuries Classification using Computer Vision

Abstract

"Knee injury is a common and frequently incapacitating problem that affects people of all ages, genders, and lifestyles. The knee joint is particularly sensitive to damage because of its complexity and the pressures that sports, vocations, and daily activities use on it. The most effective way to identify a knee injury is through magnetic resonance imaging (MRI). Understanding a knee MRI is a time-consuming and highly sensitive task. Depending on the radiologist's experience and skill, error rates can vary. To overcome those issues, the author proposed an automated knee MRI classification system using computer vision. Computer vision models like ResNet, AlexNet, VGG, and DenseNet are commonly used for knee MRI classification tasks. However advanced computer vision models InceptionNet are rarely used for knee MRI classification tasks. Some previous studies were used only for one type of knee injury. In this research, the author proposed a new model using InceptionNet for three types of knee injuries (ACL, meniscus, and knee abnormality). The author trained nine models, corresponding to three injuries and three planes using InceptionNet and modified the layers of the architecture with the pre-trained weights from ImageNet. The author finally achieved an accuracy ranging from 75% to 95% accuracy for each model."