Identify malaria parasite using pattern recognition technique

Suwalka, Isha; Sanadhya, Ankit; Mathur, Anirudh; Chouhan, Mahendra

doi:10.1109/iccca.2012.6179129

Cited by 18 publications

(10 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In 1970s, Sodeman et al 22 investigated the effect of fluorochrome staining in identifying the malaria parasites at low-level infection. It has been shown that fluorochrome staining is more sensitive and less time-consuming than Romanowsky and Giemsa staining methods 23–25 but requires considerable practice and training, and suffers from artifacts including photobleaching and phototoxicity. 26,27 Moreover, fluorescence microscopes are more expensive than standard light microscopes, which is a factor in tropical resource-poor regions where malaria is endemic.…”

Section: Staining Methodsmentioning

confidence: 99%

Image analysis and machine learning for detecting malaria

Poostchi

Silamut

Maude

et al. 2018

Translational Research

396

238

View full text Add to dashboard Cite

Malaria remains a major burden on global health, with roughly 200 million cases worldwide and more than 400,000 deaths per year. Besides biomedical research and political efforts, modern information technology is playing a key role in many attempts at fighting the disease. One of the barriers toward a successful mortality reduction has been inadequate malaria diagnosis in particular. To improve diagnosis, image analysis software and machine learning methods have been used to quantify parasitemia in microscopic blood slides. This article gives an overview of these techniques and discusses the current developments in image analysis and machine learning for microscopic malaria diagnosis. We organize the different approaches published in the literature according to the techniques used for imaging, image preprocessing, parasite detection and cell segmentation, feature computation, and automatic cell classification. Readers will find the different techniques listed in tables, with the relevant articles cited next to them, for both thin and thick blood smear images. We also discussed the latest developments in sections devoted to deep learning and smartphone technology for future malaria diagnosis.

show abstract

Section: Staining Methodsmentioning

confidence: 99%

Image analysis and machine learning for detecting malaria

Poostchi

Silamut

Maude

et al. 2018

Translational Research

396

238

View full text Add to dashboard Cite

show abstract

“…Most of the research studies surveyed have used Giemsa stained smear slide images though other staining options like Leishman staining (Makkapati et al [5], Das et al [6]) and Romanowsky staining (Suwalka et al [7]) have been used in few studies. The staining of cells are important for parasite detection for both manual and automated systems.…”

Section: Literature Reviewmentioning

confidence: 99%

Malaria Parasitaemia Estimation � A new perspective (Preprint)

Bandyopadhyay¹,

Nag²,

Basu³

2018

Preprint

View full text Add to dashboard Cite

show abstract

“…Parasite type Diaz et al (2009), Anggraini et al (2011a, Hanif et al (2011), Purwar et al (2011, Sio et al (2007), Elter et al (2011), Mavandadi et al (2012), Kareem et al (2012aKareem et al ( , 2012b, Vink et al (2013), Premaratne et al, (2003), Lee et al (2011), Luengo-Oroz et al (2012, Anggraini et al (2011b), Linder et al (2014) P. falciparum Ghosh et al (2011a) Das et al (2011), Abdul-Nasir et al (2012, von Mühlen (2004) P. vivax Das et al (2012Das et al ( , 2013, Prasad et al (2012), Makkapati & Rao, (2011), Ross et al (2006, Kaewkamnerd et al (2012), Yunda et al (2011), Maity et al (2012, Khan et al (2014) P. falciparum and P. vivax Suwalka et al (2012), Khan et al (2011), Soni (2011, Soni et al (2011), Komagal et al (2013), Ahirwar et al (2012), Tek et al (2006Tek et al ( , 2009Tek et al ( , 2010, Malihi et al (2013), Gitonga et al (2014), Thung & Suwardi (2011) P. falciparum, P. vivax, P. malariae and P. ovale Ma et al (2010), Proudfoot et al (2008) P. yoelii,…”

Section: Authorsmentioning

confidence: 99%

“…,Maity et al (2012),Soni et al (2011),Sheeba et al (2013),Khan et al (2011), Suwalka et al (2012,Panchbhai et al (2012),Ma et al (2010),Mavandadi et al (2012),Špringl (2009),Ahirwar et al, (2012),Kareem et al, (2012aKareem et al, ( , 2012b,Vink et al (2013), Ghosh et al (2011b), Abdul-Nasir et al (2012), Le et al (2008), Annaldas et al (2014), Mehrjou et al (2013), Khan et al (2014), Premaratne et al (2003), Anggraini et al (2011b), Zou et al (2010), Wahyuningrum & Indrawan (2012), Gering & Atkinson (2004), Linder et al (2014) Thin smear It is basically used for identification of malaria infected stages as well as types of parasitic infection and percentage of parasitemia Hanif et al (2011), Kaewkamnerd et al (2012), Elter et al (2011), Yunda et al (2011), Toha & Ngah (2007), Khan et al (2014), Frean (2009), Luengo-Oroz et al (2012), Arco et al (2015)…”

mentioning

confidence: 99%

Computational microscopic imaging for malaria parasite detection: a systematic review

Das

Mukherjee

Chakraborty

2015

Journal of Microscopy

View full text Add to dashboard Cite

Malaria, being an epidemic disease, demands its rapid and accurate diagnosis for proper intervention. Microscopic image-based characterization of erythrocytes plays an integral role in screening of malaria parasites. In practice, microscopic evaluation of blood smear image is the gold standard for malaria diagnosis; where the pathologist visually examines the stained slide under the light microscope. This visual inspection is subjective, error-prone and time consuming. In order to address such issues, computational microscopic imaging methods have been given importance in recent times in the field of digital pathology. Recently, such quantitative microscopic techniques have rapidly evolved for abnormal erythrocyte detection, segmentation and semi/fully automated classification by minimizing such diagnostic errors for computerized malaria detection. The aim of this paper is to present a review on enhancement, segmentation, microscopic feature extraction and computer-aided classification for malaria parasite detection.

show abstract

Identify malaria parasite using pattern recognition technique

Cited by 18 publications

References 8 publications

Image analysis and machine learning for detecting malaria

Image analysis and machine learning for detecting malaria

Malaria Parasitaemia Estimation � A new perspective (Preprint)

Computational microscopic imaging for malaria parasite detection: a systematic review

Contact Info

Product

Resources

About