Leukemia Classification using a Convolutional Neural Network of AML Images


  • Karrar A. Kadhim ᵃSchool of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia; ᵇComputer Techniques Engineering Department, Faculty of Information Technology, Imam Ja’afar Al-Sadiq University, Baghdad, Iraq
  • Fallah H Najjar Department of Computer Systems Techniques, Technical Institute of Najaf, Al-Furat Al-Awsat Technical University, Iraq
  • Ali Abdulhussein Waad ᵃSchool of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia; ᵇComputer Techniques Engineering Department, Faculty of Information Technology, Imam Ja’afar Al-Sadiq University, Baghdad, Iraq
  • Ibrahim H Al-Kharsan Department of Computer Technical Engineering, College of Technical Engineering, Islamic University, Najaf 54001, Iraq
  • Zaid Nidhal Khudhair ᵃSchool of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia; ᵇComputer Techniques Engineering Department, Faculty of Information Technology, Imam Ja’afar Al-Sadiq University, Baghdad, Iraq
  • Ali Aqeel Salim Laser Center and Physics Department, Faculty of Science, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia




Leukemia, classification, CNN, AML, ALL-ADB1


Among the most pressing issues in the field of illness diagnostics is identifying and diagnosing leukemia at its earliest stages, which requires accurate distinction of malignant leukocytes at a low cost. Leukemia is quite common, yet laboratory diagnostic centres often lack the necessary technology to diagnose the disease properly, and the available procedures take a long time. They are considering the efficacy of machine learning (ML) in illness diagnostics and that deep learning as a machine learning method is becoming critical. This study proposes a convolutional neural network (CNN) deep learning model for leukemia diagnosis utilizing the AML (acute myeloid leukemia) dataset. The classification using the proposed method achieved results that exceeded 98% accuracy, the sensitivity of 94.73% and specificity of 98.87%.


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