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Publication
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| Title of Article |
Deep Cellular Automata-Based Feature Extraction for Classification of the Breast Cancer Image |
| Date of Acceptance |
12 May 2023 |
| Journal |
| Title of Journal |
Applied Sciences |
| Standard |
ISI |
| Institute of Journal |
MDPI AG (Basal, Switzerland) |
| ISBN/ISSN |
2076-3417 |
| Volume |
2023 |
| Issue |
Volume. 13 Issue. 10 |
| Month |
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| Year of Publication |
2023 |
| Page |
(6081) 1-22 |
| Abstract |
Feature extraction is an important step in classification. It directly results in an improvement
of classification performance. Recent successes of convolutional neural networks (CNN) have
revolutionized image classification in computer vision. The outstanding convolution layer of CNN
performs feature extraction to obtain promising features from images. However, it faces the overfitting
problem and computational complexity due to the complicated structure of the convolution layer
and deep computation. Therefore, this research problem is challenging. This paper proposes a novel
deep feature extraction method based on a cellular automata (CA) model for image classification. It
is established on the basis of a deep learning approach and multilayer CA with two main processes.
Firstly, in the feature extraction process, multilayer CA with rules are built as the deep feature
extraction model based on CA theory. The model aims at extracting multilayer features, called feature
matrices, from images. Then, these feature matrices are used to generate score matrices for the deep
feature model trained by the CA rules. Secondly, in the decision process, the score matrices are
flattened and fed into the fully connected layer of an artificial neural network (ANN) for classification.
For performance evaluation, the proposed method is empirically tested on BreaKHis, a popular
public breast cancer image dataset used in several promising and popular studies, in comparison with
the state-of-the-art methods. The experimental results show that the proposed method achieves the
better results up to 7.95% improvement on average when compared with the state-of-the-art methods.
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| Keyword |
breast cancer; feature extraction; deep cellular automata; image classification |
| Author |
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| Reviewing Status |
มีผู้ประเมินอิสระ |
| Status |
ตีพิมพ์แล้ว |
| Level of Publication |
นานาชาติ |
| citation |
false |
| Part of thesis |
true |
| Attach file |
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| Citation |
0
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Journal Publication
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