A Semi-supervised Deep Learning Method for Cervical Cell Classification
Currently, the Thinprep cytologic test (TCT) is the most popular cervical cancer cytology test technique. It can detect precancerous conditions and microbial infections. However, this technique entirely relies on manual operation and doctors’ naked eye observation, resulting in a heavy workload and...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Analytical Cellular Pathology |
| Online Access: | http://dx.doi.org/10.1155/2022/4376178 |
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| _version_ | 1832550379533893632 |
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| author | Siqi Zhao Yongjun He Jian Qin Zixuan Wang |
| author_facet | Siqi Zhao Yongjun He Jian Qin Zixuan Wang |
| author_sort | Siqi Zhao |
| collection | DOAJ |
| description | Currently, the Thinprep cytologic test (TCT) is the most popular cervical cancer cytology test technique. It can detect precancerous conditions and microbial infections. However, this technique entirely relies on manual operation and doctors’ naked eye observation, resulting in a heavy workload and low accuracy rate. Recently, automatic pathological diagnosis has been developed to solve this problem. Cervical cell classification is a key technology in the intelligent cervical cancer diagnosis system. Training a deep neural network-based classification model requires a large amount of data. However, cervical cell labeling requires specialized physicians and the cost of labeling is high, resulting in a lack of sufficient labeling data in this field. To address this problem, we propose a method to ensure high accuracy in cervical cell classification with a small amount of labeled data by introducing manual features and a voting mechanism to achieve data expansion in semi-supervised learning. The method consists of three main steps, using a clarity function to filter out high-quality cervical cell images, annotating a small amount of them, and balancing the training data using a voting mechanism. With a small amount of labeled data, the accuracy of the proposed method in this paper can reach to 91.94%. |
| format | Article |
| id | doaj-art-d43136f9f64141aba569c93e67af8a03 |
| institution | Kabale University |
| issn | 2210-7185 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Analytical Cellular Pathology |
| spelling | doaj-art-d43136f9f64141aba569c93e67af8a032025-02-03T06:06:53ZengWileyAnalytical Cellular Pathology2210-71852022-01-01202210.1155/2022/4376178A Semi-supervised Deep Learning Method for Cervical Cell ClassificationSiqi Zhao0Yongjun He1Jian Qin2Zixuan Wang3School of Computer Science and TechnologySchool of Computer Science and TechnologySchool of Computer Science and TechnologySchool of Computer Science and TechnologyCurrently, the Thinprep cytologic test (TCT) is the most popular cervical cancer cytology test technique. It can detect precancerous conditions and microbial infections. However, this technique entirely relies on manual operation and doctors’ naked eye observation, resulting in a heavy workload and low accuracy rate. Recently, automatic pathological diagnosis has been developed to solve this problem. Cervical cell classification is a key technology in the intelligent cervical cancer diagnosis system. Training a deep neural network-based classification model requires a large amount of data. However, cervical cell labeling requires specialized physicians and the cost of labeling is high, resulting in a lack of sufficient labeling data in this field. To address this problem, we propose a method to ensure high accuracy in cervical cell classification with a small amount of labeled data by introducing manual features and a voting mechanism to achieve data expansion in semi-supervised learning. The method consists of three main steps, using a clarity function to filter out high-quality cervical cell images, annotating a small amount of them, and balancing the training data using a voting mechanism. With a small amount of labeled data, the accuracy of the proposed method in this paper can reach to 91.94%.http://dx.doi.org/10.1155/2022/4376178 |
| spellingShingle | Siqi Zhao Yongjun He Jian Qin Zixuan Wang A Semi-supervised Deep Learning Method for Cervical Cell Classification Analytical Cellular Pathology |
| title | A Semi-supervised Deep Learning Method for Cervical Cell Classification |
| title_full | A Semi-supervised Deep Learning Method for Cervical Cell Classification |
| title_fullStr | A Semi-supervised Deep Learning Method for Cervical Cell Classification |
| title_full_unstemmed | A Semi-supervised Deep Learning Method for Cervical Cell Classification |
| title_short | A Semi-supervised Deep Learning Method for Cervical Cell Classification |
| title_sort | semi supervised deep learning method for cervical cell classification |
| url | http://dx.doi.org/10.1155/2022/4376178 |
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