Parasite worm egg automatic detection in microscopy stool image based on Faster R-CNN

Viet, Ngo Quoc; ThanhTuyen, Dang Thi; Hoang, Trinh Huy

doi:10.1145/3310986.3311014

Cited by 19 publications

(12 citation statements)

References 6 publications

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“…38 shows deep learning models used in microorganism detection and the publication year of each model. As one of the classical deep learning models, CNN and its derivative models are often used in the task of microorganism detection, such as CNN mentioned in [164], [163], [160], R-CNN mentioned in [166], Faster R-CNN mentioned in [61], [30], [169], [168], YOLO mentioned in [166], [171], Mask R-CNN mentioned in [31]. Among them, Faster R-CNN is the most commonly used method.…”

Section: Analysis Of Deep Learning Based Methodsmentioning

confidence: 99%

“…With the development of the field of machine learn-ing, researches used traditional machine learning methods appear gradually, such as artificial neural network (ANN) [27], support vector machine (SVM) [28] and genetic algorithm-neural network (GA-NN) [29]. In recent years, deep learning-based methods become very popular, such as faster regionconvolutional neural networks (Faster R-CNN) [30] and Mask R-CNN [31]. In addition, many new methods can be applied in microorganism detection, such as single shot detector (SSD) [32].…”

Section: Motivationmentioning

confidence: 99%

“…The second method consists of two steps. One is a data augmentation step, including blurring, cropping, and horizontal rota- In [30], to detect parasite eggs, an automatic algorithm by applying Faster R-CNN is proposed. Fig.…”

Section: Related Workmentioning

confidence: 99%

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A State-of-the-art Survey of Object Detection Techniques in Microorganism Image Analysis: From Classical Methods to Deep Learning Approaches

Ma¹,

Li²,

Rahaman³

et al. 2021

Preprint

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Microorganisms play a vital role in human life. Therefore, microorganism detection is of great significance to human beings. However, the traditional manual microscopic detection methods have the disadvantages of long detection cycle, low detection accuracy in large orders, and great difficulty in detecting uncommon microorganisms. Therefore, it is meaningful to apply computer image analysis technology to the field of microorganism detection. Computer image analysis can realize high-precision and high-efficiency detection of microorganisms. In this review, first,we analyse the existing microorganism detection methods in chronological order, from traditional image processing and traditional machine learning to deep learning methods. Then, we analyze and summarize these existing methods and introduce some potential methods, including visual transformers. In the end, the future development direction and challenges of microorganism detection are discussed. In general, we have summarized 137 related technical papers from 1985 to the present. This review will help researchers have a more comprehensive understanding of the development process, research status, and future trends in the field of microorganism detection and provide a reference for researchers in other fields.

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Section: Analysis Of Deep Learning Based Methodsmentioning

confidence: 99%

Section: Motivationmentioning

confidence: 99%

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A State-of-the-art Survey of Object Detection Techniques in Microorganism Image Analysis: From Classical Methods to Deep Learning Approaches

Ma¹,

Li²,

Rahaman³

et al. 2021

Preprint

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“…For parasitic diseases, the systems used microscopic images of blood smears to detect Plasmodium species [31e37], mostly Plasmodium falciparum, and microscopic images of stools to identify intestinal helminths [38,39]. Although the reported performances were correct (reported sensitivity between 85% and 99% and specificity between 95% and 99%) for these articles, the exclusion of smears that are difficult to interpret and the use of manually cropped images currently limit the use of ML systems in this field.…”

Section: Microorganism Detection Identification and Quantificationmentioning

confidence: 99%

Machine learning in the clinical microbiology laboratory: has the time come for routine practice?

Peiffer-Smadja

Dellière

Rodriguez

et al. 2020

Clinical Microbiology and Infection

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Background: Machine learning (ML) allows the analysis of complex and large data sets and has the potential to improve health care. The clinical microbiology laboratory, at the interface of clinical practice and diagnostics, is of special interest for the development of ML systems. Aims: This narrative review aims to explore the current use of ML In clinical microbiology. Sources: References for this review were identified through searches of MEDLINE/PubMed, EMBASE, Google Scholar, biorXiv, arXiV, ACM Digital Library and IEEE Xplore Digital Library up to November 2019. Content: We found 97 ML systems aiming to assist clinical microbiologists. Overall, 82 ML systems (85%) targeted bacterial infections, 11 (11%) parasitic infections, nine (9%) viral infections and three (3%) fungal infections. Forty ML systems (41%) focused on microorganism detection, identification and quantification, 36 (37%) evaluated antimicrobial susceptibility, and 21 (22%) targeted the diagnosis, disease classification and prediction of clinical outcomes. The ML systems used very diverse data sources: 21 (22%) used genomic data of microorganisms, 19 (20%) microbiota data obtained by metagenomic sequencing, 19 (20%) analysed microscopic images, 17 (18%) spectroscopy data, eight (8%) targeted gene sequencing, six (6%) volatile organic compounds, four (4%) photographs of bacterial colonies, four (4%) transcriptome data, three (3%) protein structure, and three (3%) clinical data. Most systems used data from high-income countries (n ¼ 71, 73%) but a significant number used data from low-and middle-income countries (n ¼ 36, 37%). Performance measures were reported for the 97 ML systems, but no article described their use in clinical practice or reported impact on processes or clinical outcomes. Implications: In clinical microbiology, ML has been used with various data sources and diverse practical applications. The evaluation and implementation processes represent the main gap in existing ML systems, requiring a focus on their interpretability and potential integration into real-world settings.

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“…The one-stage detector, on the other hand, had a high inference speed because it directly predicted the bounding boxes without the RPN step ( Jiao et al, 2019 ). For eight classes of human parasitic eggs in stool images, an automatic parasite-worm egg detection method based on the Faster R-CNN (regions with CNN features) two-stage detector was studied and reported 97.67% in mAP ( Viet, ThanhTuyen & Hoang, 2019 ). This work motivated us to apply object detection model in the recognition of intestinal parasite eggs.…”

Section: Introductionmentioning

confidence: 99%

Automatic recognition of parasitic products in stool examination using object detection approach

Naing

Boonsang

Chuwongin

et al. 2022

PeerJ Computer Science

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Background Object detection is a new artificial intelligence approach to morphological recognition and labeling parasitic pathogens. Due to the lack of equipment and trained personnel, artificial intelligence innovation for searching various parasitic products in stool examination will enable patients in remote areas of undeveloped countries to access diagnostic services. Because object detection is a developing approach that has been tested for its effectiveness in detecting intestinal parasitic objects such as protozoan cysts and helminthic eggs, it is suitable for use in rural areas where many factors supporting laboratory testing are still lacking. Based on the literatures, the YOLOv4-Tiny produces faster results and uses less memory with the support of low-end GPU devices. In comparison to the YOLOv3 and YOLOv3-Tiny models, this study aimed to propose an automated object detection approach, specifically the YOLOv4-Tiny model, for automatic recognition of intestinal parasitic products in stools. Methods To identify protozoan cysts and helminthic eggs in human feces, the three YOLO approaches; YOLOv4-Tiny, YOLOv3, and YOLOv3-Tiny, were trained to recognize 34 intestinal parasitic classes using training of image dataset. Feces were processed using a modified direct smear method adapted from the simple direct smear and the modified Kato-Katz methods. The image dataset was collected from intestinal parasitic objects discovered during stool examination and the three YOLO models were trained to recognize the image datasets. Results The non-maximum suppression technique and the threshold level were used to analyze the test dataset, yielding results of 96.25% precision and 95.08% sensitivity for YOLOv4-Tiny. Additionally, the YOLOv4-Tiny model had the best AUPRC performance of the three YOLO models, with a score of 0.963. Conclusion This study, to our knowledge, was the first to detect protozoan cysts and helminthic eggs in the 34 classes of intestinal parasitic objects in human stools.

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Parasite worm egg automatic detection in microscopy stool image based on Faster R-CNN

Cited by 19 publications

References 6 publications

A State-of-the-art Survey of Object Detection Techniques in Microorganism Image Analysis: From Classical Methods to Deep Learning Approaches

A State-of-the-art Survey of Object Detection Techniques in Microorganism Image Analysis: From Classical Methods to Deep Learning Approaches

Machine learning in the clinical microbiology laboratory: has the time come for routine practice?

Automatic recognition of parasitic products in stool examination using object detection approach

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