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Detection of Iron Deficiency Anemia by Medical Images: a Comparative Study of Machine Learning Algorithms by Peter Appiahene; Justice Williams Asare; Emmanuel Timmy Donkoh; Giovanni Dimauro; Rosalia Maglietta is a Biochemistry, Genetics and Molecular Biology article available to read on EtoBox.

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## Abstract ## Background Anemia is one of the global public health problems that affect children and pregnant women. Anemia occurs when the level of red blood cells within the body decreases or when the structure of the red blood cells is destroyed or when the Hb level in the red blood cell is below the normal threshold, which results from one or more increased red cell destructions, blood loss, defective cell production or a depleted sum of Red Blood Cells. ## Methods The method used in this study is divided into three phases: the datasets were gathered, which is the palm, pre-processed the image, which comprised; Extracted images, and augmented images, segmented the Region of Interest of the images and acquired their various components of the CIE L\*a\*b\* colour space (also referred to as the CIELAB), and finally developed the proposed models for the detection of anemia using the various algorithms, which include CNN, k-NN, Nave Bayes, SVM, and Decision Tree. The experiment utilized 527 initial datasets, rotation, flipping and translation were utilized and augmented the dataset to 2635. We randomly divided the augmented dataset into 70%, 10%, and 20% and trained, validated and

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Author

Peter Appiahene; Justice Williams Asare; Emmanuel Timmy Donkoh; Giovanni Dimauro; Rosalia Maglietta

Publisher

Springer Science and Business Media LLC

Published

2023

Language

EN

Field

Biochemistry, Genetics and Molecular Biology (Life Sciences)