Computerized detection of leukocytes in microscopic leukorrhea images

Zhang, Jing; Zhong, Ya; Wang, Xiangzhou; Ni, Guangming; Du, Xiaohui; Liu, Juanxiu; Liu, Lin; Liu, Yong

doi:10.1002/mp.12381

Cited by 14 publications

(10 citation statements)

References 19 publications

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“…The method used a convolutional neural network architecture based on Inception-v3 and principal component analysis, it achieves high-average precision. Zhang et al 20 applied a modified convolutional neural network to detect leukocytes in the microscopic leukorrhea image. After image segmentation and intelligent classification, the method achieved high sensitivity and accuracy.…”

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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“…As compared with the same morphological method used in context [6], the segmentation method we used only had four operators in different directions, with faster calculation speeds and more accurate segmentation. We also tested the bottom-hat transform method [3] used for segmentation.…”

Section: Resultsmentioning

confidence: 99%

“…At the same time, Inception has the wider and deeper network architecture. The CNN model is used in the literature [6], but the CNN structure used is too simple and has many parameters. It is efficient for single-target detection but it is not suitable for multi-target detection.…”

Section: Resultsmentioning

confidence: 99%

“…Manik et al [5] extracted the cell from a colorful image using the segment and morphology method, then computed eight different features for the ANN, and finally got an accuracy of 98.9% from a total sample of 90. Zhang et al [6] combined two methods to distinguish the leukocytes and impurities. One method was feature extraction and SVM, which reached an accuracy of 92.5%; the other method was CNN, with an accuracy of 89.5%.…”

Section: Introductionmentioning

confidence: 99%

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Automatic classification of cells in microscopic fecal images using convolutional neural networks

Liu

Wang

et al. 2019

Bioscience Reports

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The analysis of fecal-type components for clinical diagnosis is important. The main examination involves the counting of red blood cells (RBCs), white blood cells (WBCs), and molds under the microscopic. With the development of machine vision, some vision-based detection schemes have been proposed. However, these methods have a single target for detection, with low detection efficiency and low accuracy. We proposed an algorithm to identify the visible image of fecal composition based on intelligent deep learning. The algorithm mainly includes region proposal and candidate recognition. In the process of segmentation, we proposed a morphology extraction algorithm in a complex background. As for the candidate recognition, we proposed a new convolutional neural network (CNN) architecture based on Inception-v3 and principal component analysis (PCA). This method achieves high-average Precision of 90.7%, which is better than the other mainstream CNN models. Finally, the images within the rectangle marks were obtained. The total time for detection of an image was roughly 1200 ms. The algorithm proposed in the present paper can be integrated into an automatic fecal detection system.

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“…The lack of an automatic recognition algorithm for organic components under the microscope seriously restricts the automation of routine stool analysis. Recently, deep learning technology has been successfully used in image classi cation, object detection and other computer vision tasks (Afridi et al, 2017), (Zhang et al, 2017). Compared with traditional machine learning methods, convolutional neural networks automatically extract image features, simplify and avoid unnecessary image preprocessing, and improve the validity and accuracy of detection (Simonyan and Zisserman, 2014;Szegedy et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Fast and Accurate Automated Recognition of the Dominant Cells From Fecal Images Based on Faster R-CNN

Zhang

et al. 2021

Preprint

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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.

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Computerized detection of leukocytes in microscopic leukorrhea images

Cited by 14 publications

References 19 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

Automatic classification of cells in microscopic fecal images using convolutional neural networks

Fast and Accurate Automated Recognition of the Dominant Cells From Fecal Images Based on Faster R-CNN

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