{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T15:21:05Z","timestamp":1781104865324,"version":"3.54.1"},"reference-count":18,"publisher":"IGI Global Scientific Publishing","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,4]]},"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.4018\/ijhisi.2019040105","type":"journal-article","created":{"date-parts":[[2019,1,30]],"date-time":"2019-01-30T14:26:29Z","timestamp":1548858389000},"page":"66-87","source":"Crossref","is-referenced-by-count":33,"title":["A Hybrid Deep Learning and Handcrafted Feature Approach for Cervical Cancer Digital Histology Image Classification"],"prefix":"10.4018","volume":"14","author":[{"given":"Haidar A","family":"AlMubarak","sequence":"first","affiliation":[{"name":"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"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joe","family":"Stanley","sequence":"additional","affiliation":[{"name":"Missouri University of Science and Technology, Rolla, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peng","family":"Guo","sequence":"additional","affiliation":[{"name":"Missouri University of Science and Technology, Rolla, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rodney","family":"Long","sequence":"additional","affiliation":[{"name":"Lister Hill National Center for Biomedical Communications for National Library of Medicine, Bethesda, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sameer","family":"Antani","sequence":"additional","affiliation":[{"name":"Lister Hill National Center for Biomedical Communications for National Library of Medicine, Bethesda, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"George","family":"Thoma","sequence":"additional","affiliation":[{"name":"Lister Hill National Center for Biomedical Communications for National Library of Medicine, Bethesda, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rosemary","family":"Zuna","sequence":"additional","affiliation":[{"name":"Department of Pathology for the University of Oklahoma Health Sciences Center, Oklahoma City, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shelliane","family":"Frazier","sequence":"additional","affiliation":[{"name":"University of Missouri Health Care, Columbia, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"William","family":"Stoecker","sequence":"additional","affiliation":[{"name":"The Dermatology Center, Missouri University of Science and Technology, Rolla, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2432","reference":[{"key":"IJHISI.2019040105-0","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2017.09.044"},{"key":"IJHISI.2019040105-1","unstructured":"Borovicka, J. (2003). Circle detection using Hough transforms Course Project: COMS30121- Image Processing and Computer Vision. In Image Processing and Computer Vision course. Retrieved from http:\/\/linux.fjfi.cvut.cz\/pinus\/bristol\/imageproc\/hw1\/report.pdf"},{"key":"IJHISI.2019040105-2","first-page":"411","article-title":"Mitosis Detection in Breast Cancer Histology Images using Deep Neural Networks","author":"D. C.Ciresan","year":"2013","journal-title":"Medical Image Computing and Computer-Assisted Intervention"},{"key":"IJHISI.2019040105-3","doi-asserted-by":"crossref","unstructured":"Codella, N., Nguyen, Q.-B., Pankanti, S., Gutman, D., Helba, B., Halpern, A., & Smith, J. R. (2016). Deep learning ensembles for melanoma recognition in dermoscopy images. IBM Journal of Research and Development, 61(4). Retrieved from http:\/\/arxiv.org\/abs\/1610.04662","DOI":"10.1147\/JRD.2017.2708299"},{"key":"IJHISI.2019040105-4","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2013.08.001"},{"key":"IJHISI.2019040105-5","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2010.1525"},{"key":"IJHISI.2019040105-6","doi-asserted-by":"publisher","DOI":"10.1016\/j.ygyno.2005.07.037"},{"key":"IJHISI.2019040105-7","doi-asserted-by":"publisher","DOI":"10.4103\/2153-3539.197193"},{"key":"IJHISI.2019040105-8","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2015.2483318"},{"key":"IJHISI.2019040105-9","doi-asserted-by":"crossref","unstructured":"He, L., Long, L. R., Antani, S., & Thoma, G. (2010). Computer assisted diagnosis in histopathology. Sequence and Genome Analysis: Methods and Applications, 3, 271\u2013287. Retrieved from http:\/\/iconceptpress.com\/download\/paper\/100531205733.pdf","DOI":"10.1117\/2.1201011.003358"},{"key":"IJHISI.2019040105-10","doi-asserted-by":"publisher","DOI":"10.1109\/CBMS.2004.1311774"},{"key":"IJHISI.2019040105-11","doi-asserted-by":"publisher","DOI":"10.1002\/1096-9896(2000)9999:9999<::AID-PATH708>3.0.CO;2-I"},{"key":"IJHISI.2019040105-12","unstructured":"Krizhevsky, A., Sutskever, I., & Hinton, G. (2012). ImageNet Classification with Deep Convolutional Neural Networks. In Advances in Neural Information and Processing Systems (NIPS) (Vol. 25, pp. 1097\u20131105)."},{"key":"IJHISI.2019040105-13","doi-asserted-by":"publisher","DOI":"10.5244\/C.29.41"},{"key":"IJHISI.2019040105-14","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21387"},{"key":"IJHISI.2019040105-15","doi-asserted-by":"publisher","DOI":"10.4103\/jpi.jpi_74_17"},{"key":"IJHISI.2019040105-16","doi-asserted-by":"publisher","DOI":"10.1002\/ijc.27532"},{"key":"IJHISI.2019040105-17","doi-asserted-by":"publisher","DOI":"10.1109\/JSTSP.2008.2011157"}],"container-title":["International Journal of Healthcare Information Systems and Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.igi-global.com\/viewtitle.aspx?TitleId=222731","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,5]],"date-time":"2022-05-05T17:52:16Z","timestamp":1651773136000},"score":1,"resource":{"primary":{"URL":"http:\/\/services.igi-global.com\/resolvedoi\/resolve.aspx?doi=10.4018\/IJHISI.2019040105"}},"subtitle":[""],"short-title":[],"issued":{"date-parts":[[2019,4]]},"references-count":18,"journal-issue":{"issue":"2"},"URL":"https:\/\/doi.org\/10.4018\/ijhisi.2019040105","relation":{},"ISSN":["1555-3396","1555-340X"],"issn-type":[{"value":"1555-3396","type":"print"},{"value":"1555-340X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,4]]}}}