Contreras-Ramírez Michael, Sora-Cardenas Jhonathan, Colorado-Salamanca Claudia, Ovalle-Bracho Clemencia, Suárez Daniel R
Facultad de Ingeniería, Pontificia Universidad Javeriana, Bogotá 110231, Colombia.
Hospital Universitario Centro Dermatológico Federico Lleras Acosta ESE, Bogotá 110231, Colombia.
Sensors (Basel). 2024 Dec 21;24(24):8180. doi: 10.3390/s24248180.
Cutaneous leishmaniasis is a parasitic disease that poses significant diagnostic challenges due to the variability of results and reliance on operator expertise. This study addresses the development of a system based on machine learning algorithms to detect spp. parasite in direct smear microscopy images, contributing to the diagnosis of cutaneous leishmaniasis. Starting with acquiring and labeling 500 images, an experimental design was implemented, including preprocessing and segmentation techniques such as Otsu, local thresholding, and Iterative Global Minimum Search (IGMS) to improve parasite detection. The phenotypic features of the parasites were extracted, focusing on morphology, texture, and color. Machine learning models (ANN, SVM, and RF) optimized through Grid Search were applied for classification. The model with the best results was a Support Vector Machine (SVM), achieving a sensitivity of 91.87% and a specificity of 89.21% at the crop level. Compared with previous studies, these results highlight the relevance and consistency of the methodology used, supporting the initial hypothesis. This suggests that machine learning techniques offer a promising path toward improving the diagnosis of cutaneous leishmaniasis.
皮肤利什曼病是一种寄生虫病，由于结果的变异性以及对操作人员专业知识的依赖，给诊断带来了重大挑战。本研究致力于开发一种基于机器学习算法的系统，用于在直接涂片显微镜图像中检测利什曼原虫属寄生虫，以辅助皮肤利什曼病的诊断。从获取并标记500张图像开始，实施了一项实验设计，包括采用大津法、局部阈值处理和迭代全局最小搜索（IGMS）等预处理和分割技术，以提高寄生虫检测效果。提取了寄生虫的表型特征，重点关注形态、纹理和颜色。应用通过网格搜索优化的机器学习模型（人工神经网络、支持向量机和随机森林）进行分类。结果最佳的模型是支持向量机（SVM），在作物水平上实现了91.87%的灵敏度和89.21%的特异性。与先前的研究相比，这些结果突出了所用方法的相关性和一致性，支持了最初的假设。这表明机器学习技术为改善皮肤利什曼病的诊断提供了一条有前景的途径。
