Home
  • English
  • ÄŒeÅ¡tina
  • Deutsch
  • Español
  • Français
  • Gàidhlig
  • LatvieÅ¡u
  • Magyar
  • Nederlands
  • Português
  • Português do Brasil
  • Suomi
  • Log In
    New user? Click here to register. Have you forgotten your password?
Home
  • Browse Our Collections
  • Publications
  • Researchers
  • Research Data
  • Institutions
  • Statistics
    • English
    • ÄŒeÅ¡tina
    • Deutsch
    • Español
    • Français
    • Gàidhlig
    • LatvieÅ¡u
    • Magyar
    • Nederlands
    • Português
    • Português do Brasil
    • Suomi
    • Log In
      New user? Click here to register. Have you forgotten your password?
  1. Home
  2. Resources
  3. UniMAP Index Publications
  4. Publications 2023
  5. Counting Non-Overlapping Abnormal Cervical Cells in Whole Slide Images
 
Options

Counting Non-Overlapping Abnormal Cervical Cells in Whole Slide Images

Journal
6th Iraqi International Conference on Engineering Technology and its Applications, IICETA 2023
Date Issued
2023-01-01
Author(s)
Badarneh A.
Alzuet A.
Wan Azani Wan Mustafa
Universiti Malaysia Perlis
Alquran H.
Alsalatie M.
Mohammed F.F.
Alkhayyat A.
DOI
10.1109/IICETA57613.2023.10351426
Abstract
Cervical cancer is one of the most common cancer among women globally. The Pap smear test has been widely used to detect cervical cancers according to the morphological characteristics of the cell nuclei on the micrograph. The aim of this paper is to count the non-overlapping abnormal cervical cells in whole slide images automatically by employing various image techniques. The proposed approach consists of four main steps; image enhancement, transform the extended minima, remove small pixels, and count the number of abnormal cells in the image. The proposed system used 250 cervical pap smear images where the overlap between cells is minimal. The performance of the proposed system is evaluated based on comparing the manual counting and automating counting over whole images. Therefore, the accuracy is evaluated mainly on the difference between manual and automated, and it is 92.5%. The proposed method can be used in laboratory to decrease the false positive rates in counting abnormal cells.
Funding(s)
Ministry of Higher Education, Malaysia
Subjects
  • Accuracy evaluation |...

File(s)
Research repository notification.pdf (4.4 MB)
google-scholar
Views
Downloads
  • About Us
  • Contact Us
  • Policies