Automatic classification and segmentation of blast cells using deep transfer learning and active contours.

Journal: International journal of laboratory hematology
Published Date: Oct 1, 2024

Abstract

INTRODUCTION: Acute lymphoblastic leukemia (ALL) presents a formidable challenge in hematological malignancies, necessitating swift and precise diagnostic techniques for effective intervention. The conventional manual microscopy of blood smears, although widely practiced, suffers from significant limitations including labor-intensity and susceptibility to human error, particularly in distinguishing the subtle differences between normal and leukemic cells.

Authors

  • Divine Senanu Ametefe
    Wireless Communication Technology Group, College of Engineering, School of Electrical Engineering, Universiti Teknologi MARA (UiTM), Shah Alam, Malaysia.
  • Suzi Seroja Sarnin
    Wireless Communication Technology Group, College of Engineering, School of Electrical Engineering, Universiti Teknologi MARA (UiTM), Shah Alam, Malaysia.
  • Darmawaty Mohd Ali
    Wireless Communication Technology Group, College of Engineering, School of Electrical Engineering, Universiti Teknologi MARA (UiTM), Shah Alam, Malaysia.
  • George Dzorgbenya Ametefe
    Department of Biotechnology, College of Science, Engineering and Technology, Osun State University, Osogbo, Nigeria.
  • Dah John
    College of Computing, Informatics and Mathematics, Universiti Teknologi MARA (UiTM), Puncak Perdana, Malaysia.
  • Abdulmalik Adozuka Aliu
    College of Built Environment, Universiti Teknologi MARA (UiTM), Shah Alam, Malaysia.
  • Zadok Zoreno
    Faculty of Pharmaceutical Sciences, University of Jos, Jos, Nigeria.

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