Red Blood Cells Abnormality Classification: Deep Learning Architecture versus Support Vector Machine

Aliyu, Hajara Abdulkarim; Sudirman, Rubita; Razak, Mohd Azhar Abdul; Wahab, Muhamad Amin Abd

doi:10.30880/ijie.2018.10.07.004

Cited by 6 publications

(2 citation statements)

References 13 publications

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“…The result showed that the backpropagation neural network yields higher accuracy than CNN [19]. The comparison result of SVM and AlexNet in classifying color images of erythrocytes was reported by Aliyu et al The result showed that SVM yields higher accuracy than AlexNet [21]. In leukocyte classification, Qin et al presented a fine-grinned leukocyte classification method using a deep residual neural network [22].…”

Section: Related Workmentioning

confidence: 94%

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: 94%

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

et al. 2020

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“…Aliyu et al also comparing the performance of deep learning and SVM to classify erythrocytes. The result showed that SVM superior to deep learning [12]. One reason for deep learning performance is lower than backpropagation neural networks, and SVM performance is that the data is scantiness for the training process in deep learning.…”

Section: Introductionmentioning

confidence: 99%

Erythrocyte Classification using Multi Layer Perceptron, Naïve Bayes Classifier, RBF Network and SVM

Tyas,

Hartati,

Harjoko

et al. 2019

IJEAT

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Several diseases can be diagnosed based on the appearance of abnormal erythrocytes, among others anaemia and thalassemia. Process of examination peripheral blood smear manually is time-consuming and subjective. Currently, the process of examination peripheral blood smear by laboratory assistants can be assisted with digital image processing technology so that it can speed up the examination time and avoid subjectivity. This research begins with the process of microscopic image acquisition, then preprocessing, segmentation, feature extraction and classification. The microscopic image acquisition is carried out using an additional special camera on a microscope. In this study, we used peripheral blood smear of thalassemia patients and healthy individuals. We convert the RGB image to grayscale image and perform the median filtering in the preprocessing stage. In the segmentation stage, we used the watershed distance transform method. As a segmentation result, we got 7108 erythrocyte images consisting of nine types of erythrocytes. In feature extraction, we used shape, color and texture characteristics to represent erythrocytes. The combination of these three features is used as classifier’s input. One crucial stage in digital image processing technology is object classification. In this study, erythrocyte classification is done by comparing four types of the classifier to determine the best classifier performance in this case. Multi-Layer Perceptron (MLP), Naïve Bayes classifier, RBF Network, and SVM used as classifiers in this study. Experimental results showed that MLP got the highest performance with 89.6% accuracy, 89.3% precision and 89.6% recall. Furthermore SVM came in second place, followed by RBF Network and Naïve Bayes classifier.

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Geometric Feature-Based Classification of Anemia in Blood Smear Images using Decision Tree

Ette,

Nagalla,

Addanki

et al. 2024

2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT)

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Red Blood Cells Abnormality Classification: Deep Learning Architecture versus Support Vector Machine

Cited by 6 publications

References 13 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

Erythrocyte Classification using Multi Layer Perceptron, Naïve Bayes Classifier, RBF Network and SVM

Geometric Feature-Based Classification of Anemia in Blood Smear Images using Decision Tree

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