A Hybrid Deep Learning and Handcrafted Feature Approach for Cervical Cancer Digital Histology Image Classification

Haidar A. AlMubarak, Joe Stanley, Peng Guo, Rodney Long, Sameer Antani, George Thoma, Rosemary Zuna, Shelliane Frazier and William Stoecker
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Haidar A. AlMubarak: Missouri University of Science and Technology, Rolla, USA & Advanced Lab for Intelligent Systems Rresearch, Department of Computer Engineering, College of Information and Computer Sciences, King Saud University, Riyadh, Saudi Arabia & Electrical and Computer Engineering Department, Missouri University of Science and Technology, Rolla, USA
Joe Stanley: Missouri University of Science and Technology, Rolla, USA
Peng Guo: Missouri University of Science and Technology, Rolla, USA
Rodney Long: Lister Hill National Center for Biomedical Communications for National Library of Medicine, Bethesda, USA
Sameer Antani: Lister Hill National Center for Biomedical Communications for National Library of Medicine, Bethesda, USA
George Thoma: Lister Hill National Center for Biomedical Communications for National Library of Medicine, Bethesda, USA
Rosemary Zuna: Department of Pathology for the University of Oklahoma Health Sciences Center, Oklahoma City, USA
Shelliane Frazier: University of Missouri Health Care, Columbia, USA
William Stoecker: The Dermatology Center, Missouri University of Science and Technology, Rolla, USA

International Journal of Healthcare Information Systems and Informatics (IJHISI), 2019, vol. 14, issue 2, 66-87

Abstract: Cervical cancer is the second most common cancer affecting women worldwide but is curable if diagnosed early. Routinely, expert pathologists visually examine histology slides for assessing cervix tissue abnormalities. A localized, fusion-based, hybrid imaging and deep learning approach is explored to classify squamous epithelium into cervical intraepithelial neoplasia (CIN) grades for a dataset of 83 digitized histology images. Partitioning the epithelium region into 10 vertical segments, 27 handcrafted image features and rectangular patch, sliding window-based convolutional neural network features are computed for each segment. The imaging and deep learning patch features are combined and used as inputs to a secondary classifier for individual segment and whole epithelium classification. The hybrid method achieved a 15.51% and 11.66% improvement over the deep learning and imaging approaches alone, respectively, with a 80.72% whole epithelium CIN classification accuracy, showing the enhanced epithelium CIN classification potential of fusing image and deep learning features.

Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:igg:jhisi0:v:14:y:2019:i:2:p:66-87

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