Multi Distance and Angle Models of the Gray Level Co-occurrence Matrix (GLCM) to Extract the Acute Lymphoblastic Leukemia (ALL) Images

doi:10.22266/ijies2021.1231.32

Cited by 4 publications

(9 citation statements)

References 27 publications

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“…In this section, we also compare the others, i.e., as seen in Table 7. Several methods have been implemented by other researchers, such as shape features [3], Multi distance of GLCM [4], CNN and SVM [7], Pretrained deep convolutional neural networks [11], AlexNet [13], ensemble network [18], convolutional and recurrent neural network [19], and hypercomplex-valued convolutional neural networks [21].…”

Section: Comparing To Other Methodsmentioning

confidence: 99%

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A Pyramid Model of Convolutional Neural Network to Classify Acute Lymphoblastic Leukemia Images

2022

IJIES

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Many researchers have classified acute lymphoblastic leukemia using several methods. One of the methods is a convolutional neural network. However, the limitation of the convolutional neural network is a large number of trainable parameters updated. The paper proposes a new architecture based on the convolutional neural network. We have designed and implemented a convolutional neural network with different kernels, where we increase the number of kernels like pyramid models. We utilized the final convolution to conduct the fully connected, followed by the SoftMax function to classify the image. We have evaluated our proposed architecture using acute lymphoblastic leukemia image database (ALL-IDB2). The results show that our proposed method produced the average Accuracy, Precision, and Recall of 99.17%, 99.33%, and 99%, respectively. It has outperformed other models, i.e., shape features, Multi distance of GLCM, CNN and SVM, Pretrained deep convolutional neural networks, AlexNet, ensemble network, convolutional and recurrent neural network, and hypercomplex-valued convolutional neural networks.

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Section: Comparing To Other Methodsmentioning

confidence: 99%

“…The weakness of the paper has been improved by image segmentation enhancement and followed by texture features generated, i.e., homogeneity, entropy, energy, and contrast. Four features can increase the accuracy to 96.67% [4]. It shows that the improvement of the image segmentation process only increases 1.29% accuracy.…”

Section: Introductionmentioning

confidence: 91%

A Pyramid Model of Convolutional Neural Network to Classify Acute Lymphoblastic Leukemia Images

2022

IJIES

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“…The accuracy rate has been boosted to 96.67% due to adding these four characteristics. Even though image segmentation enhancement produced a mere 1.29% increase in accuracy, research suggests that refining segmentation alone may not substantially improve [3].…”

Section: Introductionmentioning

confidence: 96%

“…Researchers have conducted and produced numerous results using the gray level co-occurrence matrix (GLCM) regarding acute lymphoblastic Leukemia [1][2][3]. Theoretically, GLCM features can be utilized for many computer vision tasks, for instance, image classification, detection, recognition, restoration, and segmentation.…”

Section: Introductionmentioning

confidence: 99%

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A New Model: Commutative Hypercomplex – Convolutional Neural Network to Classify Acute Lymphoblastic Leukemia Images

2023

IJIES

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The purpose of the paper is to classify the Leukemia images. In this paper, we proposed a commutative model of a convolutional neural network for Leukemia image classification. We employ commutative hypercomplex modeling A[+1, -1] and A[-1, +1] to build the new model. We hire an augmentation model to enrich the image data sets for the training sets through rotation, zooming, and flipping. We evaluated our proposed method using acute lymphoblastic leukemia image database type 2 (ALL-IDB2). The results show that our proposed method has delivered the best average accuracy at 96.43% for A[+1,-1] and 97.05 for A[-1,+1]. We have measured and found maximum accuracy at 100% for A[+1,-1] and A[-1,+1]. Comparison results show that our proposed method outperformed Knearest neighbor, support vector machine-radial basis function, support vector machine-linear, support vector machinepolynomial, naïve bayes gaussian, naïve bayes complement, decision tree, and colour hybrid modelling.

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Optimized Support Vector Machine Using Whale Optimization Algorithm for Acute Lymphoblastic Leukemia Detection from Microscopic Blood Smear Images

Saikia,

Sarma,

Shuleenda Devi

2024

SN COMPUT. SCI.

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Multi Distance and Angle Models of the Gray Level Co-occurrence Matrix (GLCM) to Extract the Acute Lymphoblastic Leukemia (ALL) Images

Cited by 4 publications

References 27 publications

A Pyramid Model of Convolutional Neural Network to Classify Acute Lymphoblastic Leukemia Images

A Pyramid Model of Convolutional Neural Network to Classify Acute Lymphoblastic Leukemia Images

A New Model: Commutative Hypercomplex – Convolutional Neural Network to Classify Acute Lymphoblastic Leukemia Images

Optimized Support Vector Machine Using Whale Optimization Algorithm for Acute Lymphoblastic Leukemia Detection from Microscopic Blood Smear Images

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