Version 1
: Received: 17 July 2023 / Approved: 17 July 2023 / Online: 17 July 2023 (11:32:55 CEST)
How to cite:
Magotra, N. A Step Toward the Future: Using Machine Learning to Detect Leukemia.. Preprints2023, 2023071114. https://doi.org/10.20944/preprints202307.1114.v1
Magotra, N. A Step Toward the Future: Using Machine Learning to Detect Leukemia.. Preprints 2023, 2023071114. https://doi.org/10.20944/preprints202307.1114.v1
Magotra, N. A Step Toward the Future: Using Machine Learning to Detect Leukemia.. Preprints2023, 2023071114. https://doi.org/10.20944/preprints202307.1114.v1
APA Style
Magotra, N. (2023). A Step Toward the Future: Using Machine Learning to Detect Leukemia.. Preprints. https://doi.org/10.20944/preprints202307.1114.v1
Chicago/Turabian Style
Magotra, N. 2023 "A Step Toward the Future: Using Machine Learning to Detect Leukemia." Preprints. https://doi.org/10.20944/preprints202307.1114.v1
Abstract
Leukemia is a cancer of the bone marrow, a spongy tissue that secretes into the bones and serves as the site for the production of blood cells. One of the most prevalent kinds of leukemia in adults is acute myeloid leukemia (AML). Leukemia has non-specific signs and symptoms that are also similar to those of other interpersonal illnesses. The only way to accurately diagnose leukemia is by manually examining a stained blood smear or bone marrow aspirate under the microscope. However, this approach takes more time and is less precise. This paper describes a method for the automatic recognition and classification of AML in blood smears. Classification techniques include decision trees, logistic regression, support vector machines, and naive bayes.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
