2018
DOI: 10.4066/biomedicalresearch.29-18-970
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Computational approach for diagnosis of malaria through classification of malaria parasite from microscopic image of blood smear

Abstract: Malaria is disease which is affecting millions of people and it is generally detected by examining the Red Blood Corpuscles (RBC) manually using microscope. However, the manual microscopic approach is time consuming, and lack of experts in the rural area, makes diagnosis of malaria very challenging one. The reported image processing approch extent the modern digital facilities to address the demand of automation, by developing a computerised facility for the detection of malaria using image processing techniqu… Show more

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Cited by 8 publications
(12 citation statements)
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“…Niranjana et al's classification process involves extracting statistical, textural, and geometric features from the segmented greyscale images. The geometrical features used are mean, variance, and standard deviation (Sampathila, Shet, & Basu, 2018). A grey‐level co‐occurrence matrix was created to extract the texture features.…”
Section: Literature Studymentioning
confidence: 99%
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“…Niranjana et al's classification process involves extracting statistical, textural, and geometric features from the segmented greyscale images. The geometrical features used are mean, variance, and standard deviation (Sampathila, Shet, & Basu, 2018). A grey‐level co‐occurrence matrix was created to extract the texture features.…”
Section: Literature Studymentioning
confidence: 99%
“…The obtained accuracy for hybrid classifier, Naive Bayes, SVM, k‐NN, and ANN are 96.54, 84.92, 89.84, 93.15, and 94.15%, respectively (Devi et al, 2018). Sampathila et al (2018) acquired thin blood smear stained by Leishman and digitized. The digitized image is preprocessed using gray world normalization and an adaptive median filter to remove unwanted illumination artifacts, respectively.…”
Section: Literature Studymentioning
confidence: 99%
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“…Previous studies applied methods based on histogram thresholding (color similarity, Otsu, local and adaptive thresholding etc. ), and morphological operations as given in Table 2 and [4][5][6][7][8][9][10][11][12]. Most studies used only the green color channel for detection of parasites.…”
Section: Introductionmentioning
confidence: 99%