research-article

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

Authors:

Dang Thi ThanhTuyen,

Trinh Huy HoangAuthors Info & Claims

ICMLSC '19: Proceedings of the 3rd International Conference on Machine Learning and Soft Computing

Pages 197 - 202

https://doi.org/10.1145/3310986.3311014

Published: 25 January 2019 Publication History

Abstract

This paper proposed a method based on Faster R-CNN for detection of human parasite eggs in stool images. The shapes, and patterns of parasite worm in egg micro images are very diversity, therefore proposing and choosing the good model to detect them is necessary to help the doctors discover the potential disease by worm in human. To be sure for the proposal, we executed many various experiments, and retrieved dataset from two independent resources. The training set is retrieved in standard biology image library, meanwhile the evaluation image set is retrieved from real patients. The precision, recall and other values evaluated in the experiments represented the effectiveness of the method. The various experiments with the outstanding results proved the correctness of the proposal.

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Cited By

Xu WZhai QLiu JXu XHua J(2024)A lightweight deep-learning model for parasite egg detection in microscopy imagesParasites & Vectors10.1186/s13071-024-06503-217:1Online publication date: 6-Nov-2024
https://doi.org/10.1186/s13071-024-06503-2
Zia M(2024)Adaptive Enhancement Network With Border Injection for Animal Parasite Eggs DetectionIEEE Access10.1109/ACCESS.2024.349424912(170161-170175)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3494249
Kumar SArif TAhamad GChaudhary AAli MIslam A(2024)Improving faster R-CNN generalization for intestinal parasite detection using cycle-GAN based data augmentationDiscover Applied Sciences10.1007/s42452-024-05941-y6:5Online publication date: 11-May-2024
https://doi.org/10.1007/s42452-024-05941-y
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cover image ACM Other conferences

ICMLSC '19: Proceedings of the 3rd International Conference on Machine Learning and Soft Computing

January 2019

268 pages

ISBN:9781450366120

DOI:10.1145/3310986

Copyright © 2019 ACM.

© 2019 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 January 2019

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ICMLSC 2019

ICMLSC 2019: 2019 the 3rd International Conference on Machine Learning and Soft Computing

January 25 - 28, 2019

Da Lat, Viet Nam

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Cited By

Xu WZhai QLiu JXu XHua J(2024)A lightweight deep-learning model for parasite egg detection in microscopy imagesParasites & Vectors10.1186/s13071-024-06503-217:1Online publication date: 6-Nov-2024
https://doi.org/10.1186/s13071-024-06503-2
Zia M(2024)Adaptive Enhancement Network With Border Injection for Animal Parasite Eggs DetectionIEEE Access10.1109/ACCESS.2024.349424912(170161-170175)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3494249
Kumar SArif TAhamad GChaudhary AAli MIslam A(2024)Improving faster R-CNN generalization for intestinal parasite detection using cycle-GAN based data augmentationDiscover Applied Sciences10.1007/s42452-024-05941-y6:5Online publication date: 11-May-2024
https://doi.org/10.1007/s42452-024-05941-y
Mishra KKavitha CKannan D(2024)Identification and Classification of Intestinal Parasitic Eggs in Animals Through Microscopic Image AnalysisEmergent Converging Technologies and Biomedical Systems10.1007/978-981-99-8646-0_45(571-581)Online publication date: 25-Feb-2024
https://doi.org/10.1007/978-981-99-8646-0_45
Kumar SArif T(2024)Deep Transfer Learning in Parasites Imaging: A Systematic ReviewProceedings of Fifth International Conference on Computing, Communications, and Cyber-Security10.1007/978-981-97-7371-8_19(243-255)Online publication date: 5-Dec-2024
https://doi.org/10.1007/978-981-97-7371-8_19
Kumar SArif TAhamad GChaudhary AKhan SAli M(2023)An Efficient and Effective Framework for Intestinal Parasite Egg Detection Using YOLOv5Diagnostics10.3390/diagnostics1318297813:18(2978)Online publication date: 18-Sep-2023
https://doi.org/10.3390/diagnostics13182978
Jaya Sundar Rajasekar SJaswal GPerumal VRavi SDutt V(2023)Parasite.ai – An Automated Parasitic Egg Detection Model from Microscopic Images of Fecal Smears using Deep Learning Techniques2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI)10.1109/ACCAI58221.2023.10200869(1-9)Online publication date: 25-May-2023
https://doi.org/10.1109/ACCAI58221.2023.10200869
Joao LProença LLoiola SInácio Sdos Santos BRosa SSoares FStefano VOsaku DSuzuki CBresciani KGomes JFalcão A(2023)Toward automating the diagnosis of gastrointestinal parasites in cats and dogsComputers in Biology and Medicine10.1016/j.compbiomed.2023.107203163(107203)Online publication date: Sep-2023
https://doi.org/10.1016/j.compbiomed.2023.107203
Suwannaphong TChavana STongsom SPalasuwan DChalidabhongse TAnantrasirichai N(2023)Parasitic Egg Detection and Classification in Low-Cost Microscopic Images Using Transfer LearningSN Computer Science10.1007/s42979-023-02406-85:1Online publication date: 9-Dec-2023
https://doi.org/10.1007/s42979-023-02406-8
Zhang JMa PJiang TZhao XTan WZhang JZou SHuang XGrzegorzek MLi C(2022)SEM-RCNN: A Squeeze-and-Excitation-Based Mask Region Convolutional Neural Network for Multi-Class Environmental Microorganism DetectionApplied Sciences10.3390/app1219990212:19(9902)Online publication date: 1-Oct-2022
https://doi.org/10.3390/app12199902
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