CAM - a novel aid system to analyse the coloration quality of thick blood smears using image processing and machine learning techniques

Aug 4, 2026

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Malaria diagnosis relies on thick blood smears whose coloration quality affects parasite detection, yet the impact of smear staining variability has not been fully characterized. A machine learning framework called Coloration Analysis in Malaria (CAM) is proposed to automatically evaluate thick blood smear image coloration quality. CAM uses an image database and feature-vector selection with classifier tuning to identify the most effective model for accurate quality categorization and supportive diagnostic analysis.

LanguageEnglish

CategoryHealthcare

FormatPDF

Pages12

Reading Time30 min

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Abstract

Background

Methods

Results

Conclusions

Main content

Malaria diagnosis and the role of thick blood smears

Limitations of prior automated approaches

Fong Amaris etal. Malaria Journal (2024) 23:299 [https://doi.org/10.1186/s12936-024-05025-7](https://doi.org/10.1186/s12936-024-05025-7) Malaria Journal RESEARCH Open Access CAM: a novel aid system to analyse the coloration quality of thick blood smears using image processing and machine learning techniques W. M. Fong Amaris1,2*, Daniel R. Suárez3, Liliana J. Cortés‑Cortés4 and Carol Martinez5* Abstract Background Battling malaria’s morbidity and mortality rates demands innovative methods related to malaria diag‑ nosis. Thick blood smears (TBS) are the gold standard for diagnosing malaria, but their coloration quality is depend‑ ent on supplies and adherence to standard protocols. Machine learning has been proposed to automate diagnosis, but the impact of smear coloration on parasite detection has not yet been fully explored. Methods To develop Coloration Analysis in Malaria (CAM), an image database containing 600 images was created. The database was randomly divided into training (70%), validation (15%), and test (15%) sets. Nineteen feature vec‑ tors were studied based on variances, correlation coefficients, and histograms (specific variables from histograms, full histograms, and principal components from the histograms) . The Machine Learning Matlab Toolbox was used to select the best candidate feature vectors and machine learning classifiers. The candidate classifiers were then tuned for validation and tested to ultimately select the best one. Results This work introduces CAM, a machine learning system designed for automatic TBS image quality analysis. The results demonstrated that the cubic SVM classifier outperformed others in classifying coloration quality in TBS, achiev‑ ing a true negative rate of 95% and a true positive rate of 97% . Conclusions An image‑based approach was developed to automatically evaluate the coloration quality ofTBS. This finding highlights the potential of image‑based analysis to assess TBS coloration quality. CAM is intended to function as a supportive tool for analyzing the coloration quality of thick blood smears. Keywords Thick blood smears, Coloration quality, Image processing, Machine learning, Malaria diagnosis *Correspondence: W. M. Fong Amaris [we_fong@javeriana.edu.co](we_fong@javeriana.edu.co); [wfong110@gmail.com](wfong110@gmail.com) Carol Martinez carol. martinezluna@uni.lu 1 Pontificia Universidad Javeriana, Faculty of Engineering, Bogotá, Colombia 2 Universidade Federal do Pará, Institute of Biological Sciences, Belém, Brazil 3 Facultad de Ingeniería, Pontificia Universidad Javeriana, Bogotá, Colombia 4 Laboratory of Parasitology, National Health Institute of Colombia, Bogotá, Colombia 5 Space Robotics (SpaceR) Research Group, Interdisciplinary Centre for Security, Reliability, and Trust (SnT), University of Luxembourg, Luxembourg, Luxembourg © The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit [http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (](http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ()[http://creativeco](http://creativeco)[mmons.org/publicdomain/zero/1.0/](mmons.org/pu

FAQs

Which machine learning classifier performed best and what accuracy was reported?

The cubic SVM classifier outperformed the others for classifying coloration quality. It achieved a true negative rate of 95% and a true positive rate of 97%.

Aug 4, 2026

Upload by Oliver