convolutional neural networks; deep learning; image processing; malaria diagnosis; support vector machines; thick blood smears; Azure Stains; Humans; Support Vector Machine; Neural Networks, Computer; Algorithms; Azure Stains/chemistry; Leukocytes/parasitology; Malaria/parasitology; Malaria/diagnosis; Malaria/blood; Image Processing, Computer-Assisted/methods; Blood smears; Convolutional neural network; F1 scores; Images processing; Leukocyte detection; Parasite-; Support vectors machine; Thick blood smear; Image Processing, Computer-Assisted; Leukocytes; Malaria; Plasmodium falciparum; Staining and Labeling; Analytical Chemistry; Information Systems; Atomic and Molecular Physics, and Optics; Biochemistry; Instrumentation; Electrical and Electronic Engineering
Abstract :
[en] Malaria remains a global health concern, with 249 million cases and 608,000 deaths being reported by the WHO in 2022. Traditional diagnostic methods often struggle with inconsistent stain quality, lighting variations, and limited resources in endemic regions, making manual detection time-intensive and error-prone. This study introduces an automated system for analyzing Romanowsky-stained thick blood smears, focusing on image quality evaluation, leukocyte detection, and malaria parasite classification. Using a dataset of 1000 clinically diagnosed images, we applied feature extraction techniques, including histogram bins and texture analysis with the gray level co-occurrence matrix (GLCM), alongside support vector machines (SVMs), for image quality assessment. Leukocyte detection employed optimal thresholding segmentation utility (OTSU) thresholding, binary masking, and erosion, followed by the connected components algorithm. Parasite detection used high-intensity region selection and adaptive bounding boxes, followed by a custom convolutional neural network (CNN) for candidate identification. A second CNN classified parasites into trophozoites, schizonts, and gametocytes. The system achieved an F1-score of 95% for image quality evaluation, 88.92% for leukocyte detection, and 82.10% for parasite detection. The F1-score-a metric balancing precision (correctly identified positives) and recall (correctly detected instances out of actual positives)-is especially valuable for assessing models on imbalanced datasets. In parasite stage classification, CNN achieved F1-scores of 85% for trophozoites, 88% for schizonts, and 83% for gametocytes. This study introduces a robust and scalable automated system that addresses critical challenges in malaria diagnosis by integrating advanced image quality assessment and deep learning techniques for parasite detection and classification. This system's adaptability to low-resource settings underscores its potential to improve malaria diagnostics globally.
Disciplines :
Engineering, computing & technology: Multidisciplinary, general & others
Author, co-author :
Sora-Cardenas, Jhonathan ; Faculty of Engineering, Pontificia Universidad Javeriana, Bogotá 110311, Colombia
Fong-Amaris, Wendy M ; Faculty of Engineering, Pontificia Universidad Javeriana, Bogotá 110311, Colombia ; Programa de Doutorado em Biotecnologia, Universidade Federal do Pará, Belém 66075-110, Brazil
Salazar-Centeno, Cesar A ; Faculty of Engineering, Pontificia Universidad Javeriana, Bogotá 110311, Colombia
Castañeda, Alejandro; Faculty of Engineering, Pontificia Universidad Javeriana, Bogotá 110311, Colombia ; Computer Vision Lab, Delft University of Technology, 2628 XE Delft, The Netherlands
Martínez-Bernal, Oscar D ; Faculty of Engineering, Pontificia Universidad Javeriana, Bogotá 110311, Colombia
Suárez, Daniel R ; Faculty of Engineering, Pontificia Universidad Javeriana, Bogotá 110311, Colombia
The authors declare the following potential conflicts of interest regarding the funding of this research. This project received partial funding from the M.Sc. program in Bioengineering at Pontificia Universidad Javeriana. Additionally, funding was provided by Facebook Inc. through the CV4GC RFP Research Award Winner 3418118842, with Pontificia Universidad Javeriana ID PPTA 9053 and ID PRY 9411. However, the funders had no role in the design of the study, data collection and analysis, decision to publish, or manuscript preparation. The authors affirm that this funding did not influence the research\u2019s objectivity or integrity.We would like to express our deepest gratitude to everyone who has supported and contributed to this research. First and foremost, we sincerely thank the Master Program in Bioengineering at Pontificia Universidad Javeriana for providing the necessary resources and facilities to carry out this study. The authors thank the National Institute of Health in Colombia for supplying the Romanowsky-stained thick blood smear images. This work was partly supported by a grant from Facebook Inc. through the CV4GC RFP Research Award Winner 3418118842, and we are thankful for their financial assistance.
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