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Ma, M.W., Gao, X.S., Zhang, Z.Y., Shang, S.Y., Jin, L., Liu, P.L., Zong, H., et al. (2023) Extracting Laboratory Test Information from Paper-Based Reports. BMC Medical Informatics and Decision Making, 23, Article No. 251.
https://doi.org/10.1186/s12911-023-02346-6

has been cited by the following article:

• TITLE: Automated Extraction and Analysis of CBC Test from Scanned Images

  AUTHORS: Iman S. Alansari

  KEYWORDS: Image Processing, Optical Character Recognition, Tesseract OCR, Health Care Application

  JOURNAL NAME: Journal of Software Engineering and Applications, Vol.17 No.2, February 29, 2024

  ABSTRACT: Health care is an important part of human life and is a right for everyone. One of the most basic human rights is to receive health care whenever they need it. However, this is simply not an option for everyone due to the social conditions in which some communities live and not everyone has access to it. This paper aims to serve as a reference point and guide for users who are interested in monitoring their health, particularly their blood analysis to be aware of their health condition in an easy way. This study introduces an algorithmic approach for extracting and analyzing Complete Blood Count (CBC) parameters from scanned images. The algorithm employs Optical Character Recognition (OCR) technology to process images containing tabular data, specifically targeting CBC parameter tables. Upon image processing, the algorithm extracts data and identifies CBC parameters and their corresponding values. It evaluates the status (High, Low, or Normal) of each parameter and subsequently presents evaluations, and any potential diagnoses. The primary objective is to automate the extraction and evaluation of CBC parameters, aiding healthcare professionals in swiftly assessing blood analysis results. The algorithmic framework aims to streamline the interpretation of CBC tests, potentially improving efficiency and accuracy in clinical diagnostics.