Malaria parasite identification on thick blood film using genetic programming

Purnama, Iwan; Rahmanti, Farah Zakiyah; Purnomo, Mauridhi Hery

doi:10.1109/icici-bme.2013.6698491

Cited by 19 publications

(12 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Purnama et al [29] classified all detected object into two and six classes. Classification process performed using GP method for 120 training data and 60 testing data.…”

Section: Discussionmentioning

confidence: 99%

“…Rosado et al [21] Geometry, Color, and Texture features. Purnama et al [29] Mean, standard deviation, kurtosis, skewness and entropy of color channel combination.…”

Section: Authormentioning

confidence: 99%

“…Purnama et al [29] proposed a genetic programming (GP) method to classify parasites and non parasites objects. GP is a method to evolve computer programs [30].…”

Section: Authormentioning

confidence: 99%

“…Purnama et al [29] perform a manual crop of parasite objects as a region of interest. Extracted features from cropped images are mean, standard deviation, kurtosis, skewness and entropy of the red-green-blue channel color histogram, hue channel of the HSV histogram, and hue channel of HSI histogram.…”

Section: (3) Feature Extractionmentioning

confidence: 99%

See 3 more Smart Citations

Detection of malaria parasites in thick blood smear: A review

Azif

Nugroho²,

Wibirama³

2018

CST

View full text Add to dashboard Cite

Based on data from World Health Organization, in 2015, there are 90% of deaths caused by malaria disease in Africa, Southeast Asia and countries of eastern Mediterranean. It makes the malaria become one of the most dangerous diseases that often leads to death. To support the diagnosis of malaria, early detection of plasmodium parasite is needed. Recently, malaria diagnosis process can be done with the help of computer, or often referred to as Computer Aided Diagnosis (CAD). By utilizing the digital image from the blood staining process, digital image processing can be performed to detect the presence of malaria parasite. There are 2 types of blood smear images that can be used in the malaria diagnosis process, namely, thin blood smear images and thick blood smear images. This paper provides a review of the techniques and methods used in the diagnosis of computer-assisted malaria using thick blood smear images as a diagnostic material.

show abstract

“…Purnama et al [29] classified all detected object into two and six classes. Classification process performed using GP method for 120 training data and 60 testing data.…”

Section: Discussionmentioning

confidence: 99%

“…Rosado et al [21] Geometry, Color, and Texture features. Purnama et al [29] Mean, standard deviation, kurtosis, skewness and entropy of color channel combination.…”

Section: Authormentioning

confidence: 99%

“…Purnama et al [29] proposed a genetic programming (GP) method to classify parasites and non parasites objects. GP is a method to evolve computer programs [30].…”

Section: Authormentioning

confidence: 99%

Section: (3) Feature Extractionmentioning

confidence: 99%

See 2 more Smart Citations

Detection of malaria parasites in thick blood smear: A review

Azif

Nugroho²,

Wibirama³

2018

CST

View full text Add to dashboard Cite

show abstract

“…Penelitian terkait telah diteliti oleh Purnama E, dkk. [7] mengidentifikasi citra sediaan darah parasit malaria berdasarkan RGB chanel histogram, HSI histogram dan HSV histogram diperoleh menggunakan perhitungan mean, standar deviasi, skewness, kurtosis dan entropy. Nasir A, dkk [8] telah melakukan segmentasi untuk mendeteksi parasit malaria pada sediaan darah tipis menggunakan median filter, kemudian melakukan segmentasi moving K-means untuk menghilangkan objek yang tidak terpakai pada citra sediaan darah.…”

Section: Darah Menggunakan Metode Modified K-means (Mk-means) Ekstraunclassified

Penerapan Fitur Warna Untuk Identifikasi Plasmodium Falciparum pada Sediaan Apus Darah Menggunakan MK-Means dan Jaringan Backpropagation

hamid

2016

View full text Add to dashboard Cite

Abstract - This research proposed a system to identify Plasmodium falciparum on blood smear using the neural network backpropagation. Modified K-Means (MK-Means) is used to separate between the object with the background image because that method was able to equalize the value of fitness at all Center cluster so there is no dead center and can also cope with the local minimum value. The extraction of the features used in this study consists of color features i.e. calculation of the mean, standard deviation, skewness, curtosis and entropy of co-occurent matrix with the purpose to get the values of all the trait value image, obtained are then used to train a neural network with the backpropagation training algorithm. Method of backpropagation networks capable of acquiring knowledge even though there is no certainty, able to perform a generalization and extraction of a specific data pattern. The image of the preparations blood smear are classified using the method of neural network Backpropagation. The test results obtained from Tropozoit with the accuracy 100%, scizon 80% and gametocytes 80%. Identification is then obtained outcomes the introduction with an average accuracy of 86,66%.

show abstract

Deep Learning Applications to Cytopathology: A Study on the Detection of Malaria and on the Classification of Leukaemia Cell-Lines

Gopakumar

Subrahmanyam

2019

Handbook of Deep Learning Applications

View full text Add to dashboard Cite

Malaria parasite identification on thick blood film using genetic programming

Cited by 19 publications

References 2 publications

Detection of malaria parasites in thick blood smear: A review

Detection of malaria parasites in thick blood smear: A review

Penerapan Fitur Warna Untuk Identifikasi Plasmodium Falciparum pada Sediaan Apus Darah Menggunakan MK-Means dan Jaringan Backpropagation

Deep Learning Applications to Cytopathology: A Study on the Detection of Malaria and on the Classification of Leukaemia Cell-Lines

Contact Info

Product

Resources

About