Advances Towards Automatic Detection and Classification of Parasites Microscopic Images Using Deep Convolutional Neural Network: Methods, Models and Research Directions

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.