Journal of Pathology Informatics Journal of Pathology Informatics
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Year : 2016  |  Volume : 7  |  Issue : 1  |  Page : 51

Enhancements in localized classification for uterine cervical cancer digital histology image assessment

1 Department of Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, MO, USA
2 Lister Hill National Center for Biomedical Communications, National Library of Medicine, National Institutes of Health, DHHS, Bethesda, MD, USA
3 Department of Pathology, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
4 Surgical Pathology Department, University of Missouri Hospitals and Clinics, Columbia, MO, USA
5 Stoecker & Associates, Rolla, MO, USA

Correspondence Address:
R Joe Stanley
Department of Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, MO
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/2153-3539.197193

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Background: In previous research, we introduced an automated, localized, fusion-based approach for classifying uterine cervix squamous epithelium into Normal, CIN1, CIN2, and CIN3 grades of cervical intraepithelial neoplasia (CIN) based on digitized histology image analysis. As part of the CIN assessment process, acellular and atypical cell concentration features were computed from vertical segment partitions of the epithelium region to quantize the relative distribution of nuclei. Methods: Feature data was extracted from 610 individual segments from 61 images for epithelium classification into categories of Normal, CIN1, CIN2, and CIN3. The classification results were compared against CIN labels obtained from two pathologists who visually assessed abnormality in the digitized histology images. In this study, individual vertical segment CIN classification accuracy improvement is reported using the logistic regression classifier for an expanded data set of 118 histology images. Results: We analyzed the effects on classification using the same pathologist labels for training and testing versus using one pathologist labels for training and the other for testing. Based on a leave-one-out approach for classifier training and testing, exact grade CIN accuracies of 81.29% and 88.98% were achieved for individual vertical segment and epithelium whole-image classification, respectively. Conclusions: The Logistic and Random Tree classifiers outperformed the benchmark SVM and LDA classifiers from previous research. The Logistic Regression classifier yielded an improvement of 10.17% in CIN Exact grade classification results based on CIN labels for training-testing for the individual vertical segments and the whole image from the same single expert over the baseline approach using the reduced features. Overall, the CIN classification rates tended to be higher using the training-testing labels for the same expert than for training labels from one expert and testing labels from the other expert. The Exact class fusion- based CIN discrimination results obtained in this study are similar to the Exact class expert agreement rate.

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