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Algorithm Enhancements for Improvement of Localized Classification of Uterine Cervical Cancer Digital Histology Images

Algorithm Enhancements for Improvement of Localized Classification of Uterine Cervical Cancer Digital Histology Images

Haidar Almubarak, Peng Guo, R. Joe Stanley, Rodney Long, Sameer Antani, George Thoma, Rosemary Zuna, Shelliane R. Frazier, Randy H. Moss, William V. Stoecker, Jason Hagerty
Copyright: © 2018 |Pages: 17
ISBN13: 9781522554608|ISBN10: 1522554602|EISBN13: 9781522554615
DOI: 10.4018/978-1-5225-5460-8.ch010
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MLA

Almubarak, Haidar, et al. "Algorithm Enhancements for Improvement of Localized Classification of Uterine Cervical Cancer Digital Histology Images." Handbook of Research on Emerging Perspectives on Healthcare Information Systems and Informatics, edited by Joseph Tan, IGI Global, 2018, pp. 234-250. https://doi.org/10.4018/978-1-5225-5460-8.ch010

APA

Almubarak, H., Guo, P., Stanley, R. J., Long, R., Antani, S., Thoma, G., Zuna, R., Frazier, S. R., Moss, R. H., Stoecker, W. V., & Hagerty, J. (2018). Algorithm Enhancements for Improvement of Localized Classification of Uterine Cervical Cancer Digital Histology Images. In J. Tan (Ed.), Handbook of Research on Emerging Perspectives on Healthcare Information Systems and Informatics (pp. 234-250). IGI Global. https://doi.org/10.4018/978-1-5225-5460-8.ch010

Chicago

Almubarak, Haidar, et al. "Algorithm Enhancements for Improvement of Localized Classification of Uterine Cervical Cancer Digital Histology Images." In Handbook of Research on Emerging Perspectives on Healthcare Information Systems and Informatics, edited by Joseph Tan, 234-250. Hershey, PA: IGI Global, 2018. https://doi.org/10.4018/978-1-5225-5460-8.ch010

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Abstract

In prior research, the authors introduced an automated, localized, fusion-based approach for classifying squamous epithelium into Normal, CIN1, CIN2, and CIN3 grades of cervical intraepithelial neoplasia (CIN) from digitized histology image analysis. The image analysis approach partitioned the epithelium along the medial axis into ten vertical segments. Texture, cellularity, nuclear characterization and distribution, and acellular features were computed from each vertical segment. The individual vertical segments were CIN classified, and the individual classifications were fused to generate an image-based CIN assessment. In this chapter, image analysis techniques are investigated to improve the execution time of the algorithms and the CIN classification accuracy of the baseline algorithms. For an experimental data set of 117 digitized histology images, execution time for exact grade CIN classification accuracy was improved by 32.32 seconds without loss of exact grade CIN classification accuracy (80.34% vs. 79.49% previously reported) for this same data set.

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