[en] [en] BACKGROUND: Battling malaria's morbidity and mortality rates demands innovative methods related to malaria diagnosis. Thick blood smears (TBS) are the gold standard for diagnosing malaria, but their coloration quality is dependent 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 vectors 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, achieving 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 of TBS. 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.
Disciplines :
Engineering, computing & technology: Multidisciplinary, general & others
Author, co-author :
Fong Amaris, W M; Pontificia Universidad Javeriana, Faculty of Engineering, Bogotá, Colombia. we_fong@javeriana.edu.co ; Universidade Federal do Pará, Institute of Biological Sciences, Belém, Brazil. we_fong@javeriana.edu.co
Suárez, Daniel R; Facultad de Ingeniería, Pontificia Universidad Javeriana, Bogotá, Colombia
Cortés-Cortés, Liliana J; Laboratory of Parasitology, National Health Institute of Colombia, Bogotá, Colombia
MARTINEZ LUNA, Carol ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > Space Robotics
External co-authors :
yes
Language :
English
Title :
CAM: a novel aid system to analyse the coloration quality of thick blood smears using image processing and machine learning techniques.
Facebook Inc., CV4GC 2019 RFP Research Award Pontificia Universidad Javeriana M.Sc. program in Bioengineering at Pontificia Universidad Javeriana
Funding text :
The authors thank the Colombia National Institute of Health for supplying the thick blood smear samples used in this research, Martha Ayala for her invaluable assistance as a Malaria expert advisor, and the field laboratory staff responsible for malaria diagnosis who participated in the interviews. Lastly, we thank Professor Martha Manrique from Pontificia Universidad Javeriana for her indispensable support throughout the image acquisition process.
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