Kumar Satish, Arif Tasleem, Alotaibi Abdullah S, Malik Majid B, Manhas Jatinder
Department of Information Technology, BGSB University Rajouri, Rajouri, J&K 185131 India.
Computer Science Department, Shaqra University, Shaqra, Kingdom of Saudi Arabia.
Arch Comput Methods Eng. 2023;30(3):2013-2039. doi: 10.1007/s11831-022-09858-w. Epub 2022 Dec 3.
In the developing world, parasites are responsible for causing several serious health problems, with relatively high infections in human beings. The traditional manual light microscopy process of parasite recognition remains the golden standard approach for the diagnosis of parasitic species, but this approach is time-consuming, highly tedious, and also difficult to maintain consistency but essential in parasitological classification for carrying out several experimental observations. Therefore, it is meaningful to apply deep learning to address these challenges. Convolution Neural Network and digital slide scanning show promising results that can revolutionize the clinical parasitology laboratory by automating the process of classification and detection of parasites. Image analysis using deep learning methods have the potential to achieve high efficiency and accuracy. For this review, we have conducted a thorough investigation in the field of image detection and classification of various parasites based on deep learning. Online databases and digital libraries such as ACM, IEEE, ScienceDirect, Springer, and Wiley Online Library were searched to identify sufficient related paper collections. After screening of 200 research papers, 70 of them met our filtering criteria, which became a part of this study. This paper presents a comprehensive review of existing parasite classification and detection methods and models in chronological order, from traditional machine learning based techniques to deep learning based techniques. In this review, we also demonstrate the summary of machine learning and deep learning methods along with dataset details, evaluation metrics, methods limitations, and future scope over the one decade. The majority of the technical publications from 2012 to the present have been examined and summarized. In addition, we have discussed the future directions and challenges of parasites classification and detection to help researchers in understanding the existing research gaps. Further, this review provides support to researchers who require an effective and comprehensive understanding of deep learning development techniques, research, and future trends in the field of parasites detection and classification.
在发展中世界，寄生虫导致了若干严重的健康问题，人类感染率相对较高。传统的寄生虫识别手工光学显微镜检查方法仍然是诊断寄生虫种类的金标准方法，但这种方法耗时、极为繁琐，且难以保持一致性，不过在寄生虫学分类以进行多项实验观察方面至关重要。因此，应用深度学习来应对这些挑战具有重要意义。卷积神经网络和数字玻片扫描显示出了有前景的结果，能够通过自动化寄生虫分类和检测过程来变革临床寄生虫学实验室。使用深度学习方法进行图像分析有潜力实现高效和准确。在本次综述中，我们基于深度学习对各种寄生虫的图像检测和分类领域进行了全面调查。搜索了诸如ACM、IEEE、ScienceDirect、Springer和Wiley Online Library等在线数据库和数字图书馆，以识别足够的相关论文集。在筛选了200篇研究论文后，其中70篇符合我们的筛选标准，成为本研究的一部分。本文按时间顺序对现有的寄生虫分类和检测方法及模型进行了全面综述，从基于传统机器学习技术到基于深度学习技术展开。在本次综述中，我们还展示了机器学习和深度学习方法的总结，以及数据集细节、评估指标、方法局限性和过去十年的未来发展方向。对2012年至今的大多数技术出版物进行了审查和总结。此外，我们讨论了寄生虫分类和检测的未来方向与挑战，以帮助研究人员了解现有的研究差距。此外，本综述为那些需要有效且全面了解深度学习发展技术、寄生虫检测和分类领域研究及未来趋势的研究人员提供了支持。
