A low‐cost, automated parasite diagnostic system via a portable, robotic microscope and deep learning

Li, Yaning; Zheng, Rui; Wu, Yizhen; Chu, Kaiqin; Xu, Qianming; Sun, Mingzhai; Smith, Zachary J.

doi:10.1002/jbio.201800410

Cited by 30 publications

(14 citation statements)

References 48 publications

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“…The proposed method can achieve high average testing accuracy and improve classification accuracy. Li et al 22 proposed that a cost-effective, automated parasite diagnostic system, parasite images are automatically segmented and analyzed using a trained convolution neural network (CNN), with good performance. Lin et al 23 proposed a loss function called focal loss, which can reduce the weight of easy-to-classify samples, make the model more focused on the difficult-to-classify samples, and design the RetinaNet network model to prove its effectiveness.…”

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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“…Classification accuracy of 93.8% was achieved across the four targeted parasites in a data set of 100 fecal samples containing a minimum of 10 fecal samples for each targeted parasite. Similarly, in [14], a low-cost, automated parasite diagnostic system using fecal samples of sheep via a portable robotic microscope and a CNN based on the U-Net structure is presented. The system was trained with egg parasite morphologies of ascarid, Trichuris spp., strongyle, and Coccidia, achieving an accuracy of 92% to 96%.…”

Section: Related Workmentioning

confidence: 99%

Automatic identification of intestinal parasites in reptiles using microscopic stool images and convolutional neural networks

Parra¹,

Grijalva

Núñez

et al. 2022

PLoS ONE

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Captive environments trigger the propagation and multiplication of parasites among different reptile species, thus weakening their immune response and causing infections and diseases. Technological advances of convolutional neural networks have opened a new field for detecting and classifying diseases which have shown great potential to overcome the shortcomings of manual detection performed by experts. Therefore, we propose an approach to identify six captive reptiles parasitic agents (Ophionyssus natricis, Blastocystis sp, Oxiurdo egg, Rhytidoides similis, Strongyloides, Taenia) or the absence of such parasites from a microscope stool images dataset. Towards this end, we first use an image segmentation stage to detect the parasite within the image, which combines the Contrast Limited Adaptive Histogram Equalization (CLAHE) technique, the OTSU binarization method, and morphological operations. Then, we carry out a classification stage through MobileNet CNN under a transfer learning scheme. This method was validated on a stool image dataset containing 3616 images data samples and 26 videos from the six parasites mentioned above. The results obtained indicate that our transfer learning-based approach can learn a helpful representation from the dataset. We obtained an average accuracy of 94.26% across the seven classes (i.e., six parasitic agents and the absence of parasites), which statistically outperformed, at a 95% confidence level, a custom CNN trained from scratch.

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“…Semantic segmentation is widely used in digital pathology for applications, like tissue segmentation, nuclei segmentation and lesion detection . Similarly, semantic segmentation of microscopic images, like nonlinear multimodal images , OCT images and fluorescence images, using auto‐encoders (see section 2.4) is gathering researcher's interest. The above‐mentioned works utilize U‐net type networks, which is an auto‐encoder architecture with special connections between the encoder and the decoder network.…”

Section: Deep Learning For Biophotonic Imagingmentioning

confidence: 99%

Deep learning a boon for biophotonics?

Pradhan

Guo

Ryabchykov

et al. 2020

Journal of Biophotonics

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This review covers original articles using deep learning in the biophotonic field published in the last years. In these years deep learning, which is a subset of machine learning mostly based on artificial neural network geometries, was applied to a number of biophotonic tasks and has achieved state-of-the-art performances. Therefore, deep learning in the biophotonic field is rapidly growing and it will be utilized in the next years to obtain real-time biophotonic decision-making systems and to analyze biophotonic data in general. In this contribution, we discuss the possibilities of deep learning in the biophotonic field including image classification, segmentation, registration, pseudostaining and resolution enhancement. Additionally, we discuss the potential use of deep learning for spectroscopic data including spectral data preprocessing and spectral classification. We conclude this review by addressing the potential applications and challenges of using deep learning for biophotonic data.

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A low‐cost, automated parasite diagnostic system via a portable, robotic microscope and deep learning

Cited by 30 publications

References 48 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 identification of intestinal parasites in reptiles using microscopic stool images and convolutional neural networks

Deep learning a boon for biophotonics?

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