Observational studyScientific reports2021
Fast and accurate automated recognition of the dominant cells from fecal images based on Faster R-CNN.
Observational study in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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Who cites it
5 citing papers in PubMed.
- A Facile Method Based on Faster R-CNN for Cell Detection in Microfluidic Devices.Analytical chemistry · 2026Article
- 3-D breast nodule detection on automated breast ultrasound using faster region-based convolutional neural networks and U-Net.Scientific reports · 2023Article
- Probiotic fermentation improves the bioactivities and bioaccessibility of polyphenols inFrontiers in nutrition · 2022Article
- Morphologic identification of clinically encountered moulds using a residual neural network.Frontiers in microbiology · 2022Article
- Quantification of the Immune Content in Neuroblastoma: Deep Learning and Topological Data Analysis in Digital Pathology.International journal of molecular sciences · 2021Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
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