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 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.
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Posted 12 Jan, 2021
On 08 Jan, 2021
On 06 Jan, 2021
On 21 Dec, 2020
Fast and Accurate Automated Recognition of the Dominant Cells From Fecal Images Based on Faster R-CNN
Posted 12 Jan, 2021
On 08 Jan, 2021
On 06 Jan, 2021
On 21 Dec, 2020
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.
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