Determination of <i>Plasmodium</i> Parasite Life Stages and Species in Images of Thin Blood Smears Using Artificial Neural Network

Journals Menu

Follow SCIRP

	+1 323-425-8868
	customer@scirp.org
	+86 18163351462(WhatsApp)
	1655362766

	Paper Publishing WeChat

Open Journal of Clinical Diagnostics

ISSN Print: 2162-5816
ISSN Online: 2162-5824
www.scirp.org/journal/ojcd
E-mail: ojcd@scirp.org

Citations h5-index & Ranking*

Journals Menu

"Determination of Plasmodium Parasite Life Stages and Species in Images of Thin Blood Smears Using Artificial Neural Network"
written by Lucy Gitonga, Daniel Maitethia Memeu, Kenneth Amiga Kaduki, Mjomba Allen Christopher Kale, Njogu Samson Muriuki,
published by Open Journal of Clinical Diagnostics, Vol.4 No.2, 2014

has been cited by the following article(s):

Google Scholar
CrossRef

[1]	Computational Methods for Automated Analysis of Malaria Parasite Using Blood Smear Images: Recent Advances
	Computational …, 2022

[2]	Malaria parasite diagnosis using computational techniques: a comprehensive review
	Journal of Physics …, 2021

[3]	Caractérisation de la texture d'images multispectrales de cellules sanguines en microscopie optique: application au diagnostic du paludisme
	2021

[4]	Review of Photoacoustic Malaria Diagnostic Techniques
	2021

[5]	Morpho-Geometrical Feature Extraction of Thin Blood Smear Microphotograph for Malaria Plasmodia Species and Life Stage Determination
	2020

[6]	An Efficient Decision based Multistage Median Filter for Reducing Impulse Noise from Blood Smear Malaria Images
	2020

[7]	Kỹ thuật chẩn đoán sốt rét tự động bằng phân tích hình ảnh xét nghiệm máu
	2020

[8]	Automatic Detection of Malaria Infected Erythrocytes Based on the Concavity Point Identification and Pseudo-Valley Based Thresholding
	2020

[9]	TensorFlow for Doctors
	2019

[10]	Malaria Disease Prediction Based on Machine Learning
	2019

[11]	Cuckoo Search based Ensemble Classifier for Predictive Analysis of Malaria Infection Scope on thin Blood Smears
	2019

[12]	Cascading YOLO: Automated Malaria Parasite Detection for Plasmodium Vivax in Thin Blood Smears
	Medical Imaging 2020: Computer-Aided Diagnosis, 2019

[13]	Predictive analysis by ensemble classifier with machine learning models
	2019

[14]	Cuckoo Search based Ensemble Classifier for Predictive Analysis of Malaria Infection Scope on thin Blood Smears.
	2019

[15]	Image analysis and machine learning for detecting malaria
	Translational Research, 2018

[16]	Malaria parasite detection and cell counting for human and mouse using thin blood smear microscopy
	2018

[17]	Optimization of Region of Interest (ROI) Image of Malaria Parasites
	2018

[18]	IDENTIFICATION OF MALARIA DISEASE AND ITS STADIUM BASED ON DIGITAL IMAGE PROCESSING
	Journal of Theoretical & Applied Information Technology, 2017

[19]	Clustering approach for unsupervised segmentation of malarial Plasmodium vivax parasite
	AIP Conference Proceedings, 2017

[20]	A review on automated diagnosis of malaria parasite in microscopic blood smears images
	Multimedia Tools and Applications, 2017

[21]	IDENTIFICATION OF MALARIA DISEASE AND ITS STADIUM BASED ON DIGITAL IMAGE PROCESSING.
	Journal of Theoretical and Applied Information Technology, 2017

[22]	Hybrid classifier based life cycle stages analysis for malaria-infected erythrocyte using thin blood smear images
	Neural Computing and Applications, 2017

[23]	Automated Status Classification Of Malaria Plasmodia From Thin Blood Smears Microphotograph Using Morphogeometrical Feature Extraction
	2017

[24]	Malaria infected erythrocyte classification based on a hybrid classifier using microscopic images of thin blood smear
	Multimedia Tools and Applications, 2016

[25]	Automatic Malaria Parasite Detection and Classification using ANFIS
	2016

[26]	Computational microscopic imaging for malaria parasite detection: a systematic review
	Journal of microscopy, 2015

[1]	Predictive analysis by ensemble classifier with machine learning models International Journal of Computers and Applications, 2023 DOI:10.1080/1206212X.2019.1675019

[2]	Robust Image Processing Framework for Intelligent Multi-Stage Malaria Parasite Recognition of Thick and Thin Smear Images Diagnostics, 2023 DOI:10.3390/diagnostics13030511

[3]	Robust Image Processing Framework for Intelligent Multi-Stage Malaria Parasite Recognition of Thick and Thin Smear Images Diagnostics, 2023 DOI:10.3390/diagnostics13030511

[4]	Computational Methods for Automated Analysis of Malaria Parasite Using Blood Smear Images: Recent Advances Computational Intelligence and Neuroscience, 2022 DOI:10.1155/2022/3626726

[5]	Automatic Detection of Malaria Infected Erythrocytes Based on the Concavity Point Identification and Pseudo-Valley Based Thresholding IETE Journal of Research, 2022 DOI:10.1080/03772063.2020.1787238

[6]	Malaria Parasite Diagnosis Using Computational Techniques: A Comprehensive Review Journal of Physics: Conference Series, 2021 DOI:10.1088/1742-6596/2107/1/012031

[7]	Morpho-Geometrical Feature Extraction of Thin Blood Smear Microphotograph for Malaria Plasmodia Species and Life Stage Determination 2020 International Conference on Advanced Computer Science and Information Systems (ICACSIS), 2020 DOI:10.1109/ICACSIS51025.2020.9263220

[8]	Information Management and Big Data Communications in Computer and Information Science, 2020 DOI:10.1007/978-3-030-46140-9_8

[9]	Automatic Detection of Malaria Infected Erythrocytes Based on the Concavity Point Identification and Pseudo-Valley Based Thresholding IETE Journal of Research, 2020 DOI:10.1080/03772063.2020.1787238

[10]	Malaria Disease Prediction Based on Machine Learning 2019 IEEE International Conference on Signal, Information and Data Processing (ICSIDP), 2019 DOI:10.1109/ICSIDP47821.2019.9173011

[11]	Predictive analysis by ensemble classifier with machine learning models International Journal of Computers and Applications, 2019 DOI:10.1080/1206212X.2019.1675019

[12]	Image analysis and machine learning for detecting malaria Translational Research, 2018 DOI:10.1016/j.trsl.2017.12.004

[13]	Malaria parasite detection and cell counting for human and mouse using thin blood smear microscopy Journal of Medical Imaging, 2018 DOI:10.1117/1.JMI.5.4.044506

[14]	Hybrid classifier based life cycle stages analysis for malaria-infected erythrocyte using thin blood smear images Neural Computing and Applications, 2017 DOI:10.1007/s00521-017-2937-4

[15]	A review on automated diagnosis of malaria parasite in microscopic blood smears images Multimedia Tools and Applications, 2017 DOI:10.1007/s11042-017-4495-2

[16]	Clustering approach for unsupervised segmentation of malarial Plasmodium vivax parasite 2017 DOI:10.1063/1.5005337

[17]	Malaria infected erythrocyte classification based on a hybrid classifier using microscopic images of thin blood smear Multimedia Tools and Applications, 2016 DOI:10.1007/s11042-016-4264-7

[18]	Computational microscopic imaging for malaria parasite detection: a systematic review Journal of Microscopy, 2015 DOI:10.1111/jmi.12270

[19]	Computational microscopic imaging for malaria parasite detection: a systematic review Journal of Microscopy, 2015 DOI:10.1111/jmi.12270

Follow SCIRP

	+1 323-425-8868
	customer@scirp.org
	+86 18163351462(WhatsApp)
	1655362766

	Paper Publishing WeChat

Open Journal of Clinical Diagnostics

Open Journal of Clinical Diagnostics

Journals Menu

Home

About SCIRP

Service

Policies