Applying Deep Learning Based Super-Resolution to Knee Imaging

In the realm of modern health and healthcare, imaging tests play a pivotal role in furnishing essential information for patient diagnosis and treatment. Among these techniques, magnetic resonance imaging (MRI) stands out due to its capacity to visualize the inner workings of the human body using magnetic fields. Advancements in computing have led to enhanced efficiency and image quality in MRI scans conducted within medical facilities. Researchers have focused on MRI processing, specifically leveraging convolutional neural network (CNN) models to enhance the resolution of knee images. In this paper, two novel models have been proposed, along with different experiments and their comparative results, which have been evaluated using standard metrics. These innovative approaches aim to optimize MRI utilization, thereby enhancing diagnostic precision and patient care while mitigating the risks associated with diagnostic test resolution.