Butploy Narut, Kanarkard Wanida, Maleewong Intapan Pewpan
Dept. of Computer Engineering, Khon Kaen University, Khon Kaen 40002, Thailand.
Dept. of Parasitology, Khon Kaen University, Khon Kaen 40002, Thailand.
J Parasitol Res. 2021 Apr 26;2021:6648038. doi: 10.1155/2021/6648038. eCollection 2021.
infection affects up to 1/3 of the world population (approximately 1.4 billion people worldwide). It has been estimated that 1.5 billion cases of infection globally and 65,000 deaths occur due to . Generally, allied health classifies parasite egg type by using on microscopy-based methods that are laborious, are limited by low sensitivity, and require high expertise. However, misclassification may occur due to their heterogeneous experience. For their reason, computer technology is considered to aid humans. With the benefit of speed and ability of computer technology, image recognition is adopted to recognize images much more quickly and precisely than human beings. This research proposes deep learning for 's egg image recognition to be used as a prototype tool for parasite egg detection in medical diagnosis. The challenge is to recognize 3 types of eggs of with the optimal architecture of deep learning. The results showed that the classification accuracy of the parasite eggs is up to 93.33%. This great effectiveness of the proposed model could help reduce the time-consuming image classification of parasite egg.
感染影响着全球多达三分之一的人口（全球约14亿人）。据估计，全球因感染导致15亿例感染病例和6.5万人死亡。一般来说，联合健康专业人员通过基于显微镜的方法对寄生虫卵类型进行分类，这些方法费力、灵敏度低且需要很高的专业知识。然而，由于经验的差异可能会出现错误分类。因此，计算机技术被认为可以辅助人类。借助计算机技术的速度和能力，采用图像识别比人类能更快、更精确地识别图像。本研究提出将深度学习用于[寄生虫名称]的虫卵图像识别，以作为医学诊断中寄生虫卵检测的原型工具。挑战在于利用深度学习的最优架构识别[寄生虫名称]的3种虫卵。结果表明，寄生虫卵的分类准确率高达93.33%。所提出模型的这种高效性有助于减少寄生虫卵图像分类的耗时。
