Detection of iron deficiency anemia by medical images: a comparative study of machine learning algorithms

BioData Mining

Research

Giovanni Dimauro¹  Emmanuel Timmy Donkoh²  Justice Williams Asare³  Peter Appiahene³  Rosalia Maglietta⁴ 

[1] Coordinatore del Consiglio Di Interclasse Dei Corsi Di Studio in InformaticaDipartimento Di Informatica, Università Degli Studi Di Bari ‘Aldo Moro’, Bari, Italy;Department of Basic and Applied Biology, University of Energy and Natural Resources, Sunyani, Ghana;Department of Computer Science and Informatics, University of Energy and Natural Resources, Sunyani, Ghana;Institute of Intelligent Industrial Systems and Technologies for Advanced Manufacturing, National Research Council, Bari, Italy;

关键词: Anemia;    Image Augmentation;    Machine learning algorithms;    Red blood cell;    Palpable palm;    Region of Interest;    Non-invasive;   

DOI  :  10.1186/s13040-023-00319-z

 received in 2022-06-27, accepted in 2023-01-04,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundAnemia 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.MethodsThe 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 tested the models respectively.ResultsThe results of the study justify that the models performed appropriately when the palm is used to detect anemia, with the Naïve Bayes achieving a 99.96% accuracy while the SVM achieved the lowest accuracy of 96.34%, as the CNN also performed better with an accuracy of 99.92% in detecting anemia.ConclusionsThe invasive method of detecting anemia is expensive and time-consuming; however, anemia can be detected through the use of non-invasive methods such as machine learning algorithms which is efficient, cost-effective and takes less time. In this work, we compared machine learning models such as CNN, k-NN, Decision Tree, Naïve Bayes, and SVM to detect anemia using images of the palm. Finally, the study supports other similar studies on the potency of the Machine Learning Algorithm as a non-invasive method in detecting iron deficiency anemia.

【 授权许可】

CC BY   
© The Author(s) 2023

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