Study on Recognition Method of Label-free Red and White Cell Using Fecal Microscopic Image

Wang, Wei; Si, Miaomiao; Chen, Fuqu; Liu, Hui; Jiang, Xiaoming

doi:10.1145/3194480.3198909

Cited by 3 publications

(3 citation statements)

References 9 publications

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“…The overall recognition rate of the method is high, and the robustness is good. Wang et al 14 proposed an automatic identification method of red and white cells in fecal microscopy images. The method adopts a strategy based on the combination of logic operations for image segmentation, and the morphological processing is applied afterward to remove the imperfections of segmentation.…”

Section: Introductionmentioning

confidence: 99%

FecalNet: Automated detection of visible components in human feces using deep learning

Liu

et al. 2020

Medical Physics

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To automate the detection and identification of visible components in feces for early diagnosis of gastrointestinal diseases, we propose FecalNet, a method using multiple deep neural networks. Methods: FecalNet uses the ResNet152 residual network to extract and learn the characteristics of visible components in fecal microscopic images, acquire feature maps in combination with the feature pyramid network, apply the full convolutional network to classify and locate the fecal components, and implement the improved focal loss function to reoptimize the classification results. This allowed the complete automation of the detection and identification of the visible components in feces. Results: We validated this method using a fecal database of 1,122 patients. The results indicated a mean average precision (mAP) of 92.16% and an average recall (AR) of 93.56%. The average precision (AP) and AR of erythrocyte, leukocyte, intestinal mucosal epithelial cells, hookworm eggs, ascarid eggs, and whipworm eggs were 92.82% and 93.38%, 93.99% and 96.11%, 90.71% and 92.41%, 89.95% and 93.88%, 96.90% and 91.21%, and 88.61% and 94.37%, respectively. The average times required by the GPU and the CPU to analyze a fecal microscopic image are approximately 0.14 and 1.02 s, respectively. Conclusion: FecalNet can automate the detection and identification of visible components in feces. It also provides a detection and identification framework for detecting several other types of cells in clinical practice.

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Section: Introductionmentioning

confidence: 99%

FecalNet: Automated detection of visible components in human feces using deep learning

Liu

et al. 2020

Medical Physics

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“…Leukocytes are now segmented and identified using traditional image processing. These include watershed algorithms [ 4 ], edge detection [ 5 ], active contour models [ 6 ], and adaptive threshold segmentation [ 7 ]. However, the traditional method can only conduct single-label classification and cannot perform multilabel classification, and it still has issues such as incomplete cell segmentation and low accuracy.…”

Section: Introductionmentioning

confidence: 99%

“…However, the traditional method can only conduct single-label classification and cannot perform multilabel classification, and it still has issues such as incomplete cell segmentation and low accuracy. Edge detection is only capable of generating edge points, not completing image segmentation, and must be processed further after acquiring edge point information [ 5 ]. The active contour model relies on the initial contour being chosen correctly; otherwise, a suitable segmentation result cannot be obtained if the starting contour is incorrect [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

Leukocyte Segmentation Method Based on Adaptive Retinex Correction and U-Net

Chen

Zhu

2022

Computational and Mathematical Methods in Medicine

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To address the issues of uneven illumination and inconspicuous leukocyte properties in the gathered cell pictures, a leukocyte segmentation method based on adaptive retinex correction and U-net was proposed. The procedure begins by processing a peripheral blood image to clearly distinguish leukocytes from other components in the image. The adaptive retinex correction, which is based on multiscale retinex with colour replication (MSRCR), redefines the colour recovery function by introducing Michelson contrast. Then, the image is trained with the U-net convolutional neural network, and the leukocyte segmentation is completed. The innovation is in the manner of processing peripheral blood images, which improves the accuracy of leukocyte segmentation. This study conducts experimental evaluations on the Cellavision, BCCD, and LISC datasets. The experimental results show that the method in this study is better than the current best method, and the segmentation accuracy rate reaches 98.87%.

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Leukocyte recognition in human fecal samples using texture features

Wang

Liu

et al. 2018

J. Opt. Soc. Am. A

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Study on Recognition Method of Label-free Red and White Cell Using Fecal Microscopic Image

Cited by 3 publications

References 9 publications

FecalNet: Automated detection of visible components in human feces using deep learning

FecalNet: Automated detection of visible components in human feces using deep learning

Leukocyte Segmentation Method Based on Adaptive Retinex Correction and U-Net

Leukocyte recognition in human fecal samples using texture features

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