ALLD: Acute Lymphoblastic Leukemia Detector.

Journal: Studies in health technology and informatics
Published Date: Jan 14, 2022

Abstract

Acute Lymphoblastic Leukemia (ALL) is a life-threatening type of cancer wherein mortality rate is unquestionably high. Early detection of ALL can reduce both the rate of fatality as well as improve the diagnosis plan for patients. In this study, we developed the ALL Detector (ALLD), which is a deep learning-based network to distinguish ALL patients from healthy individuals based on blast cell microscopic images. We evaluated multiple DL-based models and the ResNet-based model performed the best with 98% accuracy in the classification task. We also compared the performance of ALLD against state-of-the-art tools utilized for the same purpose, and ALLD outperformed them all. We believe that ALLD will support pathologists to explicitly diagnose ALL in the early stages and reduce the burden on clinical practice overall.

Authors

  • Saleh Musleh
    College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar.
  • Mohammad Tariqul Islam
    Computer Science Department, Southern Connecticut State University, USA.
  • Mohammad Towfik Alam
    Department of Vascular Biology and Molecular Pathology, Faculty of Dental Medicine and Graduate School of Dental Medicine, Hokkaido University, Sapporo 060-8586, Japan.
  • Mowafa Househ
    Faculty College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar1.
  • Zubair Shah
    College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar.
  • Tanvir Alam
    College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar.

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