Classification red blood cells using support vector machine

Akrimi, Jameela Ali; Suliman, Azizah; George, Loay E.; Ahmad, Abdul Rahim

doi:10.1109/icimu.2014.7066642

Cited by 14 publications

(15 citation statements)

References 4 publications

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“…It uses optimization segmentation, edge smoothing with mean filter to get the features of the blood cells. The classification is done using SVM [9]. Another paper suggests red blood cell classification method for the SCD based on pre-extraction of the cell region and deep Convolution Neural Networks (dCNNs) [10][11][12][13][14].…”

Section: Literature Surveymentioning

confidence: 99%

Identification of Sickle Cells in Erythrocytes Images Using Shape Descriptors

L¹,

Priyadharsini²,

Beulah³

2021

Advances in Parallel Computing

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Sickle cell anemia is a blood disorder which is widespread across world with an estimate that about 30% of total population will be affected by the end of 2050. Especially in India, the frequency of sickle cell trait can reach up to 35%, thereby increasing attention over the topic for research. It is necessary to detect the disorder as much as it is for finding a cure to it. Therefore, a system that detect the sickle cells from the blood smear images or erythrocytes images is required. The affected cells change their shape from circular to sickle shape, due to the presence of typical hemoglobin called hemoglobin S. Nowadays, the elongated affected cells are identified using image processing techniques. The shape descriptors which are the vital features used to identify any shapes in an image. The contrast and brightness of blood smear images may not be consistent as the acquisition of these images depends on various factors like luminance and chrominance. To detect the sickle cells properly in the blood smear images, the histogram equalization technique is applied to improve the contrast of the image. The image is converted to binary in order to find the boundary of the cells in the image. In the binary image, each normal cell will have holes, Since the normal red blood cell have the shape of a doughnut that has been pressed in the middle slightly. The boundary of all the cells in the image is traced and the holes inside each cells are filled. Each cell is considered as a connected component for which the eccentricity is calculated. The eccentricity is the shape descriptor which is the ratio of the major axis and minor axis of the connected components. If the eccentricity is greater than threshold, the cells are identified as sickle cells.

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Section: Literature Surveymentioning

confidence: 99%

Identification of Sickle Cells in Erythrocytes Images Using Shape Descriptors

L¹,

Priyadharsini²,

Beulah³

2021

Advances in Parallel Computing

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show abstract

“…|C| 2 |O| [8, 11-13, 18, 19, 37, 38] RFactor Ratio between a circumference of radius MA 2 and the booundary of the convex hull 15,38] Modification ratio Ratio between the diameter of enclosed circle and the length of the major axis of the ellipse that fits O. MFD mFD [12,14,[16][17][18]24] Fourier descriptors (FD1,. .…”

Section: Shapementioning

confidence: 99%

“…Statistical measures based on color spaces are applied to the feature extraction of a cell. The idea is to try to differentiate various cells by color [15]. We used RGB, HSV, and CIEL*a*b* color spaces, as they are reported in the literature to be the most convenient for classification tasks [4,9,15,37,42].…”

Section: Color Featuresmentioning

confidence: 99%

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Sickle-cell disease diagnosis support selecting the most appropriate machinelearning method: Towards a general and interpretable approach for cellmorphology analysis from microscopy images

Petrovič¹,

Moyà-Alcover²,

Jaume-i-Capó³

et al. 2020

Preprint

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In this work we propose an approach to select the classification method and features, based on the state-of-the-art, with best performance for diagnostic support through peripheral blood smear images of red blood cells. In our case we used samples of patients with sickle-cell disease which can be generalized for other study cases. To trust the behavior of the proposed system, we also analyzed the interpretability. We pre-processed and segmented microscopic images, to ensure high feature quality. We applied the methods used in the literature to extract the features from blood cells and the machine learning methods to classify their morphology. Next, we searched for their best parameters from the resulting data in the feature extraction phase. Then, we found the best parameters for every classifier using Randomized and Grid search. For the sake of scientific progress, we published parameters for each classifier, the implemented code library, the confusion matrices with the raw data, and we used the public erythrocytesIDB dataset for validation. We also defined how to select the most important features for classification to decrease the complexity and the training time, and for interpretability purpose in opaque models. Finally, comparing the best performing classification methods with the state-of-the-art, we obtained better results even with interpretable model classifiers.

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“…Mashiat Fatma and Jaya Sharma proposed the method for identifying the type of leukemia usingartificial neural network. Here, HSI color model is used and K-means algorithm is applied for clustering [34,35]. Farah and Rosalina suggest K means unsupervised clustering technique for image segmentation.…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach to Detect Chronic Leukemia using Shape based Feature Extraction and Identification with Digital Image Processing

Vaghela¹,

Modi²,

Pandya³

et al. 2016

IJAIS

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In this paper, some shape based features like area, perimeter, roundness, standard deviation etc. are used to recognize different types of white blood cells like monocyte, lymphocytes, eosinophil, basophil, neutrophils etc. Using image processing techniques, result can be obtained within 3-4 minute. To perform shape base features operation, contrast of RGB image has to be increased for better detection of white cells. After recognition of each and every cell, classification is performed to detect either it is CML (Chronic Myelogenous Leukemia) or CLL (chronic Lymphocytic leukemia). This algorithm is performed on 30 images. Out of 30, it is successful on 28 images. So it gives accuracy of 93.33%.

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Classification red blood cells using support vector machine

Cited by 14 publications

References 4 publications

Identification of Sickle Cells in Erythrocytes Images Using Shape Descriptors

Identification of Sickle Cells in Erythrocytes Images Using Shape Descriptors

Sickle-cell disease diagnosis support selecting the most appropriate machinelearning method: Towards a general and interpretable approach for cellmorphology analysis from microscopy images

A Novel Approach to Detect Chronic Leukemia using Shape based Feature Extraction and Identification with Digital Image Processing

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