Fast and accurate automated recognition of the dominant cells from fecal images based on Faster R-CNN

Fecal samples can easily be collected and are representative of a person’s current health state; therefore, the demand for routine fecal examination has increased sharply. However, manual operation may pollute the samples, and low efficiency limits the general examination speed; therefore, automatic...

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Veröffentlicht in:Scientific reports 2021-05, Vol.11 (1), p.10361-10361, Article 10361
Hauptverfasser: Zhang, Jing, Wang, Xiangzhou, Ni, Guangming, Liu, Juanxiu, Hao, Ruqian, Liu, Lin, Liu, Yong, Du, Xiaohui, Xu, Fan
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Sprache:eng
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Zusammenfassung:Fecal samples can easily be collected and are representative of a person’s current health state; therefore, the demand for routine fecal examination has increased sharply. However, manual operation may pollute the samples, and low efficiency limits the general examination speed; therefore, automatic analysis is needed. Nevertheless, recognition exhaustion time and accuracy remain major challenges in automatic testing. Here, we introduce a fast and efficient cell-detection algorithm based on the Faster-R-CNN technique: the Resnet-152 convolutional neural network architecture. Additionally, a region proposal network and a network combined with principal component analysis are proposed for cell location and recognition in microscopic images. Our algorithm achieved a mean average precision of 84% and a 723 ms detection time per sample for 40,560 fecal images. Thus, this approach may provide a solid theoretical basis for real-time detection in routine clinical examinations while accelerating the process to satisfy increasing demand.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-021-89863-4