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

Efosa Osagie, Wei Ji, Na Helian

Research output: Contribution to journalArticlepeer-review

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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.

Original languageEnglish
Pages (from-to)103-110
Number of pages8
JournalJournal of Advanced Computational Intelligence and Intelligent Informatics
Volume28
Issue number1
DOIs
Publication statusPublished - 20 Jan 2024

Keywords

  • OCR challenges
  • burned-in text
  • medical image character recognition
  • medical image processing
  • medical imaging

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