Identification of Plasmodium falciparum Stages Using Support Vector Machine Method

Setianingrum, Anif Hanifa; Wardhani, Luh Kesuma; Ridwan, Anugrah; Nasution, Silvia F.

doi:10.1109/citsm47753.2019.8965413

Cited by 3 publications

(3 citation statements)

References 2 publications

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“…Three features have been extracted, namely the size of infected red blood cell (RBC), brown pigment in the parasite and texture feature using the Gray Level Co-occurrence Matrix (GLCM) technique [31].…”

Section: Feature Extractionmentioning

confidence: 99%

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An Intelligent Classification System for Trophozoite Stages in Malaria Species

Kanafiah¹,

Mashor²,

Mohamed³

et al. 2022

Intelligent Automation &Amp; Soft Computing

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Malaria is categorised as a dangerous disease that can cause fatal in many countries. Therefore, early detection of malaria is essential to get rapid treatment. The malaria detection process is usually carried out with a 100x magnification of thin blood smear using microscope observation. However, the microbiologist required a long time to identify malaria types before applying any proper treatment to the patient. It also has difficulty to differentiate the species in trophozoite stages because of similar characteristics between species. To overcome these problems, a computer-aided diagnosis system is proposed to classify trophozoite stages of Plasmodium Knowlesi (PK), Plasmodium Falciparum (PF) and Plasmodium Vivax (PV) as early species identification. The process begins with image acquisition, image processing and classification. The image processing involved contrast enhancement using histogram equalisation (HE), segmentation procedure using a combination of hue, saturation and value (HSV) color model, Otsu method and range of each red, green and blue (RGB) color selections, and feature extraction. The features consist of the size of infected red blood cell (RBC), brown pigment in the parasite, and texture using Gray Level Co-occurrence Matrix (GLCM) parts. Finally, the classification method using Multilayer Perceptron (MLP) trained by Bayesian Rules (BR) show the highest accuracy of 98.95%, rather than Levenberg Marquardt (LM) and Conjugate Gradient Backpropagation (CGP) training algorithms.

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Section: Feature Extractionmentioning

confidence: 99%

“…The GLCM method [31] was used to extract the texture of grayscale infected RBC for PF, PV, and PK. GLCM characteristics such as contrast, correlation and entropy were used to extract the texture of grayscale infected RBC image.…”

Section: Texture Feature Using Glcm Technique Of Infected Rbcmentioning

confidence: 99%

An Intelligent Classification System for Trophozoite Stages in Malaria Species

Kanafiah¹,

Mashor²,

Mohamed³

et al. 2022

Intelligent Automation &Amp; Soft Computing

View full text Add to dashboard Cite

show abstract

“…The results of the experiment showed that the proposed method reached accuracy, sensitivity, specificity, prediction value positive and prediction value negative of 96.74 ± 0.7075%, 76.77 ± 2.1441%, 99.74 ± 0.1397%, 97.84 ± 1.2514% and 96.61 ± 0.8021%, respectively. Moreover, Setianingrum [6] identified the type of Plasmodium falciparum parasite with the gray level co-occurrence matrix (GLCM) to analyze parasite morphology by recognizing the texture and extraction of the morphological features of the parasite. The results achieved an accuracy of 91.67%.…”

Section: Introductionmentioning

confidence: 99%

A Systematic Review on Automatic Detection of Plasmodium Parasite

Sumi

Nugroho

Hartanto

2021

Int. j. eng. technol. innov.

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Plasmodium parasite is the main cause of malaria which has taken many lives. Some research works have been conducted to detect the Plasmodium parasite automatically. This research aims to identify the development of current research in the area of Plasmodium parasite detection. The research uses a systematic literature review (SLR) approach comprising three stages, namely planning, conducting, and reporting. The search process is based on the keywords which were determined in advance. The selection process involves the inclusion and exclusion criteria. The search yields 45 literatures from five different digital libraries. The identification process finds out that 28 methods are applied and mainly categorizes as machine learning algorithms with performance achievements between 60% and 95%. Overall, the research of Plasmodium parasite detection today has focused on the development with artificial intelligence specifically related to machine and deep learning. These approaches are believed as the most effective approach to detect Plasmodium parasites.

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Robust Image Processing Framework for Intelligent Multi-Stage Malaria Parasite Recognition of Thick and Thin Smear Images

et al. 2023

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Malaria is a pressing medical issue in tropical and subtropical regions. Currently, the manual microscopic examination remains the gold standard malaria diagnosis method. Nevertheless, this procedure required highly skilled lab technicians to prepare and examine the slides. Therefore, a framework encompassing image processing and machine learning is proposed due to inconsistencies in manual inspection, counting, and staging. Here, a standardized segmentation framework utilizing thresholding and clustering is developed to segment parasites’ stages of P. falciparum and P. vivax species. Moreover, a multi-stage classifier is designed for recognizing parasite species and staging in both species. Experimental results indicate the effectiveness of segmenting thick smear images based on Phansalkar thresholding garnered an accuracy of 99.86%. The employment of variance and new transferring process for the clustered members, enhanced k-means (EKM) clustering has successfully segmented all malaria stages with accuracy and an F1-score of 99.20% and 0.9033, respectively. In addition, the accuracies of parasite detection, species recognition, and staging obtained through a random forest (RF) accounted for 86.89%, 98.82%, and 90.78%, respectively, simultaneously. The proposed framework enables versatile malaria parasite detection and staging with an interactive result, paving the path for future improvements by utilizing the proposed framework on all others malaria species.

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Identification of Plasmodium falciparum Stages Using Support Vector Machine Method

Cited by 3 publications

References 2 publications

An Intelligent Classification System for Trophozoite Stages in Malaria Species

An Intelligent Classification System for Trophozoite Stages in Malaria Species

A Systematic Review on Automatic Detection of Plasmodium Parasite

Robust Image Processing Framework for Intelligent Multi-Stage Malaria Parasite Recognition of Thick and Thin Smear Images

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