Anaemia cells detection based on shape signature using neural networks

Elsalamony, Hany A.

doi:10.1016/j.measurement.2017.03.012

Cited by 22 publications

(22 citation statements)

References 16 publications

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“…The classification stage used a K-Nearest Neighbor (KNN) algorithm, which produced an accuracy of 80.6% and a sensitivity of 87.6%. Elsalamony [9] used the value of AVD (absolute deviation from the center point of the object to all points on the edge of the object) and DIF (the difference between the input absolute deviation with the stored absolute value of the cell's absolute deviation). The AVD and DIF obtained from the shape signature were combined with perimeter, area, eccentricity, convex area, ratio, and solidity.…”

Section: Related Workmentioning

confidence: 99%

“…The D2 dataset consists of 2247 single erythrocyte sub-images that are a subset of the D1 dataset. The D2 dataset was produced based on threshold criteria in Elsalamony's research [9]. Both datasets consist of nine types of erythrocytes.…”

Section: Classificationmentioning

confidence: 99%

“…The first experiment used the D1 dataset to determine the effect of the selected features on classification performance. The second experiment used the D2 dataset to compare the classification performance of the proposed method with the previous work [9]. The MLP parameters used in this study were obtained based on the best experimental results using various levels of the learning rate, momentum, and epoch.…”

Section: Classificationmentioning

confidence: 99%

“…In the next stage, the performance of the classification model is evaluated using a confusion matrix that shows the relationship between the predicted class and the actual class. The second experiment aims to compare the classification results using the features in the proposed method with the classification results using features proposed in the previous work [9].…”

Section: Classificationmentioning

confidence: 99%

“…In 2017, Elsalamony [9] introduced an algorithm for detecting anemia cells, such as sickle and elliptical cells, based on geometrical shape signature using neural networks. The study classified only elliptical cells and sickle cells.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Morphological, Texture, and Color Feature Analysis for Erythrocyte Classification in Thalassemia Cases

et al. 2020

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Abnormal erythrocytes have diverse shapes. The appearance of specific erythrocyte shapes in a person's blood can indicate certain diseases, including thalassemia. We used thalassemia peripheral blood smear images and applied a segmentation process to produce single erythrocyte sub-images. Each erythrocyte has a unique shape. The selection of appropriate features to represent erythrocytes is critical for classification accuracy. We used morphological features such as moment invariants, geometry parameters of the cell and central pallor, and distance angle signature (DAS) morphological features of the cell and central pallor. We combined morphological features with texture and color features to increase the accuracy of erythrocyte classification. In this study, the multi-layer perceptron is used to classify nine shapes of erythrocytes present in thalassemia cases. The experimental results of 7108 erythrocytes indicated an accuracy of 98.11% based on the combination of features. The experimental results also show that the combination of features we proposed produced higher classification accuracy than previous work, which yielded an accuracy of 93.77%.

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Section: Related Workmentioning

confidence: 99%

Section: Classificationmentioning

confidence: 99%

Section: Classificationmentioning

confidence: 99%

Section: Classificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Morphological, Texture, and Color Feature Analysis for Erythrocyte Classification in Thalassemia Cases

et al. 2020

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Prediction of Anemia Disease Using Classification Methods

Yeruva

Gowtham

Chandana

et al. 2021

Machine Learning Technologies and Applications

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Analysis of red blood cells from peripheral blood smear images for anemia detection: a methodological review

Navya

Prasad

Singh

2022

Med Biol Eng Comput

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Anemia is a blood disorder which is caused due to inadequate red blood cells and hemoglobin concentration. It occurs in all phases of life cycle but is more dominant in pregnant women and infants. According to the survey conducted by the World Health Organization (WHO) (McLean et al., Public Health Nutr 12(4):444–454, 2009), anemia affects 1.62 billion people constituting 24.8% of the population and is considered the world’s second leading cause of illness. The Peripheral Blood Smear (PBS) examination plays an important role in evaluating hematological disorders. Anemia is diagnosed using PBS. Being the most powerful analytical tool, manual analysis approach is still in use even though it is tedious, prone to errors, time-consuming and requires qualified laboratorians. It is evident that there is a need for an inexpensive, automatic and robust technique to detect RBC disorders from PBS. Automation of PBS analysis is very active field of research that motivated many research groups to develop methods using image processing. In this paper, we present a review of the methods used to analyze the characteristics of RBC from PBS images using image processing techniques. We have categorized these methods into three groups based on approaches such as RBC segmentation, RBC classification and detection of anemia, and classification of anemia. The outcome of this review has been presented as a list of observations. Graphical abstract

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Anaemia cells detection based on shape signature using neural networks

Cited by 22 publications

References 16 publications

Morphological, Texture, and Color Feature Analysis for Erythrocyte Classification in Thalassemia Cases

Morphological, Texture, and Color Feature Analysis for Erythrocyte Classification in Thalassemia Cases

Prediction of Anemia Disease Using Classification Methods

Analysis of red blood cells from peripheral blood smear images for anemia detection: a methodological review

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