Burnt-in Text Recognition from Medical Imaging Modalities: Existing Machine Learning Practices

Abstract

In recent times, medical imaging has become a significant component of clinical diagnosis and examinations to detect and evaluate various medical conditions. The interpretation of these medical examinations and the patient's demographics are usually textual data, which is burned in on the pixel content of medical imaging modalities (MIM). Example of these MIM includes ultrasound and X-ray imaging. As artificial intelligence advances for medical applications, there is a high demand for the accessibility of these burned-in textual data for various needs. This article aims to review the significance of burned-in textual data recognition in MIM and recent research regarding the machine learning approach, challenges, and open issues for further investigation on this application. The review describes the significant problems in this study area as low resolution and background interference of textual data. Finally, the review suggests applying more advanced deep learning ensemble algorithms as possible solutions.