Helminth egg analysis platform (HEAP): An opened platform for microscopic helminth egg identification and quantification based on the integration of deep learning architectures

Lee, Chi-Ching; Huang, Po-Jung; Yeh, Yuan-Ming; Li, Pei-Hsuan; Chiu, Cheng-Hsun; Cheng, Wei-Hung; Tang, Petrus

doi:10.1016/j.jmii.2021.07.014

Cited by 14 publications

(14 citation statements)

References 16 publications

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“…Li et al developed a highly configurable instrument at a variable cost of USD 250–500 (depending on the configuration) that is capable of quantifying malaria parasites by scanning 1.5 million red blood cells per minute [ 22 ]. There have also been studies on automatic S. haematobium egg detection which focus mainly on identifying eggs in images pre-captured by professional clinical operators mostly with isolated and non-overlapping eggs in an FoV [ 23 , 24 , 25 ]. Essentially, captured images of urine samples prepared in field settings often contain a lot of artifacts such as crystals, glass debris, air bubbles, fabric fibers and human hair.…”

Section: Introductionmentioning

confidence: 99%

Schistoscope: An Automated Microscope with Artificial Intelligence for Detection of Schistosoma haematobium Eggs in Resource-Limited Settings

et al. 2022

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For many parasitic diseases, the microscopic examination of clinical samples such as urine and stool still serves as the diagnostic reference standard, primarily because microscopes are accessible and cost-effective. However, conventional microscopy is laborious, requires highly skilled personnel, and is highly subjective. Requirements for skilled operators, coupled with the cost and maintenance needs of the microscopes, which is hardly done in endemic countries, presents grossly limited access to the diagnosis of parasitic diseases in resource-limited settings. The urgent requirement for the management of tropical diseases such as schistosomiasis, which is now focused on elimination, has underscored the critical need for the creation of access to easy-to-use diagnosis for case detection, community mapping, and surveillance. In this paper, we present a low-cost automated digital microscope—the Schistoscope—which is capable of automatic focusing and scanning regions of interest in prepared microscope slides, and automatic detection of Schistosoma haematobium eggs in captured images. The device was developed using widely accessible distributed manufacturing methods and off-the-shelf components to enable local manufacturability and ease of maintenance. For proof of principle, we created a Schistosoma haematobium egg dataset of over 5000 images captured from spiked and clinical urine samples from field settings and demonstrated the automatic detection of Schistosoma haematobium eggs using a trained deep neural network model. The experiments and results presented in this paper collectively illustrate the robustness, stability, and optical performance of the device, making it suitable for use in the monitoring and evaluation of schistosomiasis control programs in endemic settings.

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Section: Introductionmentioning

confidence: 99%

Schistoscope: An Automated Microscope with Artificial Intelligence for Detection of Schistosoma haematobium Eggs in Resource-Limited Settings

et al. 2022

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“…Lee et al . [ 58 ] developed a Helminth Egg Analysis Platform (HEAP) that help medical technicians to diagnosis parasite infections. The authors integrated the various deep learning techniques (SSD, Single Shot MultiBox Detector, U-net, and Faster R-CNN, Faster Region-based Convolutional Neural Network) to recognize the helminth egg specimens as shown in Fig.…”

Section: Based Methods and Modelsmentioning

confidence: 99%

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

Kumar

Arif

Alotaibi

et al. 2022

Arch Computat Methods Eng

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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.

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“… Jiménez et al (2020) established a Helminth Egg Automatic Detector (HEAD) that is capable of differentiating seven helminth species derived from wastewater, sludge, biosolids, faeces and soils. Furthermore, Lee et al (2021) developed a Helminth Egg Analysis Platform (HEAP) with the ability to discriminate between helminth eggs of 17 species and simultaneous quantification of the faecal egg count.…”

Section: Detection Methods For Egg Contaminationmentioning

confidence: 99%

Potentials and challenges in the isolation and detection of ascarid eggs in complex environmental matrices

Waindok

Raulf

Strübe

2022

Food and Waterborne Parasitology

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Helminth egg analysis platform (HEAP): An opened platform for microscopic helminth egg identification and quantification based on the integration of deep learning architectures

Cited by 14 publications

References 16 publications

Schistoscope: An Automated Microscope with Artificial Intelligence for Detection of Schistosoma haematobium Eggs in Resource-Limited Settings

Schistoscope: An Automated Microscope with Artificial Intelligence for Detection of Schistosoma haematobium Eggs in Resource-Limited Settings

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

Potentials and challenges in the isolation and detection of ascarid eggs in complex environmental matrices

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