Pontificia Universidad Javeriana, Faculty of Engineering, Bogotá, Colombia.
Universidade Federal do Pará, Institute of Biological Sciences, Belém, Brazil.
Malar J. 2024 Oct 7;23(1):299. doi: 10.1186/s12936-024-05025-7.
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.
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.
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%.
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.
为了降低疟疾的发病率和死亡率，需要创新的疟疾诊断方法。厚血涂片（TBS）是诊断疟疾的金标准，但它的染色质量取决于耗材和是否遵守标准操作流程。已经提出了使用机器学习来实现诊断自动化，但涂片染色对寄生虫检测的影响尚未得到充分探索。
为了开发 Coloration Analysis in Malaria（CAM），我们创建了一个包含 600 张图像的图像数据库。该数据库随机分为训练集（70%）、验证集（15%）和测试集（15%）。我们研究了 19 个基于方差、相关系数和直方图的特征向量（直方图的特定变量、完整直方图和直方图的主要成分）。使用 Machine Learning Matlab 工具箱选择最佳候选特征向量和机器学习分类器。然后对候选分类器进行验证调优，并进行测试以最终选择最佳分类器。
本研究介绍了 CAM，这是一个用于自动 TBS 图像质量分析的机器学习系统。结果表明，立方 SVM 分类器在分类 TBS 染色质量方面表现优于其他分类器，真阴性率为 95%，真阳性率为 97%。
我们开发了一种基于图像的方法，用于自动评估 TBS 的染色质量。这一发现突显了基于图像的分析在评估 TBS 染色质量方面的潜力。CAM 旨在作为分析厚血涂片染色质量的辅助工具。
