A Method for Identifying Cervical Abnormal Cells Based on Sample Benchmark Values
The identification of cervical abnormal cells using deep learning methods usually requires a large amount of training data, but these data inevitably use different samples of cervical abnormal cells to participate in model training, and naturally miss the positive and abnormal intracellular controls...
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| Format: | Article |
| Language: | zho |
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Harbin University of Science and Technology Publications
2022-12-01
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| Series: | Journal of Harbin University of Science and Technology |
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| Online Access: | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2164 |
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| _version_ | 1849387569404444672 |
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| author | ZHAO Si-qi LIANG Yi-qin QIN Jian HE Yong-jun |
| author_facet | ZHAO Si-qi LIANG Yi-qin QIN Jian HE Yong-jun |
| author_sort | ZHAO Si-qi |
| collection | DOAJ |
| description | The identification of cervical abnormal cells using deep learning methods usually requires a large amount of training data, but these data inevitably use different samples of cervical abnormal cells to participate in model training, and naturally miss the positive and abnormal intracellular controls of a single sample, resulting in the fact that recognition accuracy of cervical abnormal cells is not high, and the false positive rate is high. To solve this problem, this paper proposes a method for identifying cervical abnormal cells based on sample benchmark values. Firstly, this method identifies cervical cells and segments cervical cell nucleus by Mask R-CNN model. Then we calculate the key cervical nucleus indicators, propose the concept of benchmark cells, define the sample benchmark values, and quantify the diagnostic criteria.Finally, the abnormal nucleus indicator and model information are used to complete the reclassification of abnormal cervical cells, and the abnormal cells were identified by simulating a doctor comparing the morphology of the normal cells in the sample. Experiments show that the positive cell completion rate, positive cell detection accuracy and sample detection accuracy rate on the cervical cell smear dataset reached 84.7%, 94.6% and 92.4%, respectively. |
| format | Article |
| id | doaj-art-b8a84f2ce2e649f1bc2635dbec1de2d9 |
| institution | Kabale University |
| issn | 1007-2683 |
| language | zho |
| publishDate | 2022-12-01 |
| publisher | Harbin University of Science and Technology Publications |
| record_format | Article |
| series | Journal of Harbin University of Science and Technology |
| spelling | doaj-art-b8a84f2ce2e649f1bc2635dbec1de2d92025-08-20T03:51:40ZzhoHarbin University of Science and Technology PublicationsJournal of Harbin University of Science and Technology1007-26832022-12-01270610311410.15938/j.jhust.2022.06.013A Method for Identifying Cervical Abnormal Cells Based on Sample Benchmark ValuesZHAO Si-qi0LIANG Yi-qin1QIN Jian2HE Yong-jun3School of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150080, ChinaSchool of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150080, ChinaSchool of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150080, ChinaSchool of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150080, ChinaThe identification of cervical abnormal cells using deep learning methods usually requires a large amount of training data, but these data inevitably use different samples of cervical abnormal cells to participate in model training, and naturally miss the positive and abnormal intracellular controls of a single sample, resulting in the fact that recognition accuracy of cervical abnormal cells is not high, and the false positive rate is high. To solve this problem, this paper proposes a method for identifying cervical abnormal cells based on sample benchmark values. Firstly, this method identifies cervical cells and segments cervical cell nucleus by Mask R-CNN model. Then we calculate the key cervical nucleus indicators, propose the concept of benchmark cells, define the sample benchmark values, and quantify the diagnostic criteria.Finally, the abnormal nucleus indicator and model information are used to complete the reclassification of abnormal cervical cells, and the abnormal cells were identified by simulating a doctor comparing the morphology of the normal cells in the sample. Experiments show that the positive cell completion rate, positive cell detection accuracy and sample detection accuracy rate on the cervical cell smear dataset reached 84.7%, 94.6% and 92.4%, respectively.https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2164cervical abnormal cell recognitionsample benchmark valuesdeep learningmachine learning |
| spellingShingle | ZHAO Si-qi LIANG Yi-qin QIN Jian HE Yong-jun A Method for Identifying Cervical Abnormal Cells Based on Sample Benchmark Values Journal of Harbin University of Science and Technology cervical abnormal cell recognition sample benchmark values deep learning machine learning |
| title | A Method for Identifying Cervical Abnormal Cells Based on Sample Benchmark Values |
| title_full | A Method for Identifying Cervical Abnormal Cells Based on Sample Benchmark Values |
| title_fullStr | A Method for Identifying Cervical Abnormal Cells Based on Sample Benchmark Values |
| title_full_unstemmed | A Method for Identifying Cervical Abnormal Cells Based on Sample Benchmark Values |
| title_short | A Method for Identifying Cervical Abnormal Cells Based on Sample Benchmark Values |
| title_sort | method for identifying cervical abnormal cells based on sample benchmark values |
| topic | cervical abnormal cell recognition sample benchmark values deep learning machine learning |
| url | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2164 |
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