Swin-GA-RF: genetic algorithm-based Swin Transformer and random forest for enhancing cervical cancer classification Article Swipe
Manal Abdullah Alohali
,
Nora El-Rashidy
,
Saad Alaklabi
,
Hela Elmannai
,
Saleh Alharbi
,
Hager Saleh
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3389/fonc.2024.1392301
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3389/fonc.2024.1392301
Cervical cancer is a prevalent and concerning disease affecting women, with increasing incidence and mortality rates. Early detection plays a crucial role in improving outcomes. Recent advancements in computer vision, particularly the Swin transformer, have shown promising performance in image classification tasks, rivaling or surpassing traditional convolutional neural networks (CNNs). The Swin transformer adopts a hierarchical and efficient approach using shifted windows, enabling the capture of both local and global contextual information in images. In this paper, we propose a novel approach called Swin-GA-RF to enhance the classification performance of cervical cells in Pap smear images. Swin-GA-RF combines the strengths of the Swin transformer, genetic algorithm (GA) feature selection, and the replacement of the softmax layer with a random forest classifier. Our methodology involves extracting feature representations from the Swin transformer, utilizing GA to identify the optimal feature set, and employing random forest as the classification model. Additionally, data augmentation techniques are applied to augment the diversity and quantity of the SIPaKMeD1 cervical cancer image dataset. We compare the performance of the Swin-GA-RF Transformer with pre-trained CNN models using two classes and five classes of cervical cancer classification, employing both Adam and SGD optimizers. The experimental results demonstrate that Swin-GA-RF outperforms other Swin transformers and pre-trained CNN models. When utilizing the Adam optimizer, Swin-GA-RF achieves the highest performance in both binary and five-class classification tasks. Specifically, for binary classification, it achieves an accuracy, precision, recall, and F1-score of 99.012, 99.015, 99.012, and 99.011, respectively. In the five-class classification, it achieves an accuracy, precision, recall, and F1-score of 98.808, 98.812, 98.808, and 98.808, respectively. These results underscore the effectiveness of the Swin-GA-RF approach in cervical cancer classification, demonstrating its potential as a valuable tool for early diagnosis and screening programs.
Related Topics
Concepts
Random forest
Softmax function
Computer science
Convolutional neural network
Feature selection
Artificial intelligence
Pattern recognition (psychology)
Radio frequency
Transformer
Binary classification
Cervical cancer
Classifier (UML)
Contextual image classification
Machine learning
Support vector machine
Medicine
Image (mathematics)
Cancer
Engineering
Internal medicine
Electrical engineering
Telecommunications
Voltage
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fonc.2024.1392301
- OA Status
- gold
- Cited By
- 7
- References
- 59
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4400838639
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4400838639Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3389/fonc.2024.1392301Digital Object Identifier
- Title
-
Swin-GA-RF: genetic algorithm-based Swin Transformer and random forest for enhancing cervical cancer classificationWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-07-19Full publication date if available
- Authors
-
Manal Abdullah Alohali, Nora El-Rashidy, Saad Alaklabi, Hela Elmannai, Saleh Alharbi, Hager SalehList of authors in order
- Landing page
-
https://doi.org/10.3389/fonc.2024.1392301Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.3389/fonc.2024.1392301Direct OA link when available
- Concepts
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Random forest, Softmax function, Computer science, Convolutional neural network, Feature selection, Artificial intelligence, Pattern recognition (psychology), Radio frequency, Transformer, Binary classification, Cervical cancer, Classifier (UML), Contextual image classification, Machine learning, Support vector machine, Medicine, Image (mathematics), Cancer, Engineering, Internal medicine, Electrical engineering, Telecommunications, VoltageTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
7Total citation count in OpenAlex
- Citations by year (recent)
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2025: 7Per-year citation counts (last 5 years)
- References (count)
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59Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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