Leukemia detection using Artificial Neural Networks in Images of Human Blood Sample

Authors

  • Hakar J. Mohamed Salih Department of Computer Sciences, College of Science, University of Zakho, Duhok, Iraq
  • Jahwar Y. Arif Department of Computer Sciences, College of Science, University of Zakho, Duhok, Iraq
  • Shaimaa Q. Sabri Department of Computer Sciences, College of Science, University of Zakho, Duhok, Iraq
  • Ghada A. Taqa Department of Dental Basic Sciences, College of Dentistry, University of Mosul, Mosul, Iraq
  • Ahmet Çınar Department of Computer Engineering, College of Engineering, Fırat, University, Elazığ, Türkiye

DOI:

https://doi.org/10.55145/ajbms.2024.03.02.01

Keywords:

Blood Cells, Bone Marrow, Artificial Nerves, Look at blood cell

Abstract

This article presents a preliminary report that uses minuscule images of blood tests to develop a diagnosis of leukemia. Examining through images is crucial since illnesses can be recognized and examined at an earlier stage using the images. The framework will be centered on leukemia and white blood cell illness. In fact, even the hematologist has trouble organizing the leukemic cells, and manually arranging the platelets takes a long time and is quite loose. In this way, early detection of leukemia recurrence allows the patient to receive the appropriate treatment. In order to address this problem, the framework will make use of the capabilities in small images and examine surface, geometry, shading, and quantifiable investigation modifications. These features' variations will be utilized as the classifier input. has transformed the use of images K proposes that (NN) and agglomeration. Examining a wide range of failure measures and increasing the intricacy of every system, the findings are examined. Utilizing feedforward (NN), image division is accomplished with less noise and a very sluggish conjunction rate. K-means agglomeration and (ANN) are intentionally used in this analysis to create a collection of processes that will work together to produce a much better presentation in (IS). An analysis has been conducted to determine the best rule for (IS).

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Published

2024-02-10

How to Cite

Mohamed Salih, H. J., Arif, J. Y., Sabri, S. Q., Taqa, G. A., & Çınar, A. (2024). Leukemia detection using Artificial Neural Networks in Images of Human Blood Sample. Al-Salam Journal for Medical Science, 3(2), 1–8. https://doi.org/10.55145/ajbms.2024.03.02.01

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Section

Articles