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Research Article
Fast and Accurate Automated Recognition of the Dominant Cells From Fecal Images Based on Faster R-CNN
FAN XU
Chengdu Medical College
Corresponding Author
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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 exhaust 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 and is the Resnet-152 convolutional neural network architecture. Additionally, a region proposal network and a network combined with principal component analysis were 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 40560 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.
Keywords
object detection, deep learning, cell detection, convolutional neural network
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This preprint is under consideration at Scientific Reports. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research Article
Fast and Accurate Automated Recognition of the Dominant Cells From Fecal Images Based on Faster R-CNN
FAN XU
Chengdu Medical College
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Review
Version 1
Posted 12 Jan, 2021
No community comments so far
Editor invited
On 08 Jan, 2021
Submission checks complete
On 06 Jan, 2021
First submitted
On 21 Dec, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
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 exhaust 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 and is the Resnet-152 convolutional neural network architecture. Additionally, a region proposal network and a network combined with principal component analysis were 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 40560 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.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6