Please use this identifier to cite or link to this item: http://localhost:8080/xmlui/handle/123456789/9226
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dc.contributor.authorLee, Chi-Ching-
dc.contributor.authorHuang, Po-Jung-
dc.contributor.author-Ming Yeh, Yuan-
dc.contributor.author-Hsuan Li, Pei-
dc.contributor.author-Hsun Chiu, , Cheng-
dc.contributor.authorCheng, Wei-Hung-
dc.date.accessioned2024-12-18T04:12:38Z-
dc.date.available2024-12-18T04:12:38Z-
dc.date.issued2022-06-01-
dc.identifier.issn1684-1182-
dc.identifier.urihttp://localhost:8080/xmlui/handle/123456789/9226-
dc.description.abstractAbstract Background: Millions of people throughout the world suffer from parasite infections. Traditionally, technicians use manual eye inspection of microscopic specimens to perform a parasite examination. However, manual operations have limitations that hinder the ability to obtain precise egg counts and cause inefficient identification of infected parasites on co-infections. The technician requirements for handling a large number of microscopic examinations in countries that have limited medical resources are substantial. We developed the helminth egg analysis platform (HEAP) as a user-friendly microscopic helminth eggs identification and quantification platform to assist medical technicians during parasite infection examination.Methods: Multiple deep learning strategies including SSD (Single Shot MultiBox Detector), Unet, and Faster R-CNN (Faster Region-based Convolutional Neural Network) are integrated to identify the same specimen allowing users to choose the best predictions. An image binning and egg-in-edge algorithm based on pixel density detection was developed to increase the performance. Computers with different operation systems can be gathered to lower the computation time using our easy-to-deploy software architecture. Results: A user-friendly interface is provided to substantially increase the efficiency of manual validation. To adapt to low-cost computers, we architected a distributed computing structure with high flexibilities. Conclusions: HEAP serves not only as a prediction service provider but also as a parasitic egg database of microscopic helminth egg image collection, labeling data and pretrained models. All images and labeling resources are free and accessible at http://heap.cgu.edu.tw. HEAP can also be an ideal education and training resource for helminth egg examination.en_US
dc.language.isoenen_US
dc.publisherElsevier Taiwan LLCen_US
dc.subjectHelminth;en_US
dc.subjectParasite egg examination;en_US
dc.subjectDeep learning;en_US
dc.subjectObject detection;en_US
dc.subjectWeb server;en_US
dc.subjectDatabaseen_US
dc.titleHelminth egg analysis platform (HEAP): An opened platform for microscopic helminth egg identification and quantification based on the integration of deep learning architecturesen_US
dc.typeArticleen_US
Appears in Collections:VOL 55 NO 3 2022

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