Detection of Malignancy Associated Changes in Cervical Cell Nuclei Using Feed-Forward Neural Networks
Normal cells in the presence of a precancerous lesion undergo subtle changes of their DNA distribution when observed by visible microscopy. These changes have been termed Malignancy Associated Changes (MACs). Using statistical models such as neural networks and discriminant functions it is possible...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
1997-01-01
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| Series: | Analytical Cellular Pathology |
| Online Access: | http://dx.doi.org/10.1155/1997/839686 |
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| _version_ | 1832552370243895296 |
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| author | Roger A. Kemp Calum MacAulay David Garner Branko Palcic |
| author_facet | Roger A. Kemp Calum MacAulay David Garner Branko Palcic |
| author_sort | Roger A. Kemp |
| collection | DOAJ |
| description | Normal cells in the presence of a precancerous lesion undergo subtle changes of their DNA distribution when observed by visible microscopy. These changes have been termed Malignancy Associated Changes (MACs). Using statistical models such as neural networks and discriminant functions it is possible to design classifiers that can separate these objects from truly normal cells. The correct classification rate using feed‐forward neural networks is compared to linear discriminant analysis when applied to detecting MACs. Classifiers were designed using 53 nuclear features calculated from images for each of 25,360 normal appearing cells taken from 344 slides diagnosed as normal or containing severe dysplasia. A linear discriminant function achieved a correct classification rate of 61.6% on the test data while neural networks scored as high as 72.5% on a cell‐by‐cell basis. The cell classifiers were applied to a library of 93,494 cells from 395 slides, and the results were jackknifed using a single slide feature. The discriminant function achieved a correct classification rate of 67.6% while the neural networks managed as high as 76.2%. |
| format | Article |
| id | doaj-art-8fbcea6240154982bb70cb8431767c7d |
| institution | Kabale University |
| issn | 0921-8912 1878-3651 |
| language | English |
| publishDate | 1997-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Analytical Cellular Pathology |
| spelling | doaj-art-8fbcea6240154982bb70cb8431767c7d2025-02-03T05:58:46ZengWileyAnalytical Cellular Pathology0921-89121878-36511997-01-01141314010.1155/1997/839686Detection of Malignancy Associated Changes in Cervical Cell Nuclei Using Feed-Forward Neural NetworksRoger A. Kemp0Calum MacAulay1David Garner2Branko Palcic3BC Cancer Research Centre, Vancouver BC, V5Z 1L3, CanadaBC Cancer Research Centre, Vancouver BC, V5Z 1L3, CanadaBC Cancer Research Centre, Vancouver BC, V5Z 1L3, CanadaBC Cancer Research Centre, Vancouver BC, V5Z 1L3, CanadaNormal cells in the presence of a precancerous lesion undergo subtle changes of their DNA distribution when observed by visible microscopy. These changes have been termed Malignancy Associated Changes (MACs). Using statistical models such as neural networks and discriminant functions it is possible to design classifiers that can separate these objects from truly normal cells. The correct classification rate using feed‐forward neural networks is compared to linear discriminant analysis when applied to detecting MACs. Classifiers were designed using 53 nuclear features calculated from images for each of 25,360 normal appearing cells taken from 344 slides diagnosed as normal or containing severe dysplasia. A linear discriminant function achieved a correct classification rate of 61.6% on the test data while neural networks scored as high as 72.5% on a cell‐by‐cell basis. The cell classifiers were applied to a library of 93,494 cells from 395 slides, and the results were jackknifed using a single slide feature. The discriminant function achieved a correct classification rate of 67.6% while the neural networks managed as high as 76.2%.http://dx.doi.org/10.1155/1997/839686 |
| spellingShingle | Roger A. Kemp Calum MacAulay David Garner Branko Palcic Detection of Malignancy Associated Changes in Cervical Cell Nuclei Using Feed-Forward Neural Networks Analytical Cellular Pathology |
| title | Detection of Malignancy Associated Changes in Cervical Cell Nuclei Using Feed-Forward Neural Networks |
| title_full | Detection of Malignancy Associated Changes in Cervical Cell Nuclei Using Feed-Forward Neural Networks |
| title_fullStr | Detection of Malignancy Associated Changes in Cervical Cell Nuclei Using Feed-Forward Neural Networks |
| title_full_unstemmed | Detection of Malignancy Associated Changes in Cervical Cell Nuclei Using Feed-Forward Neural Networks |
| title_short | Detection of Malignancy Associated Changes in Cervical Cell Nuclei Using Feed-Forward Neural Networks |
| title_sort | detection of malignancy associated changes in cervical cell nuclei using feed forward neural networks |
| url | http://dx.doi.org/10.1155/1997/839686 |
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