Journal
COMPLEX ADAPTIVE SYSTEMS CONFERENCE WITH THEME: ENGINEERING CYBER PHYSICAL SYSTEMS, CAS
Volume 114, Issue -, Pages 281-287Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.procs.2017.09.044
Keywords
Cervical Cancer; Convolutoin Neural Networks; Data Fusion; Image classifcation; Clinical Decision Support Systems
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Funding
- Intramural Research Program of the National Institutes of Health (NIH)
- National Library of Medicine (NLM)
- Lister Hill National Center for Biomedical Communications (LHNCBC)
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In previous research, we introduced an automated localized, fusion-based algorithm to classify squamous epithelium into Normal, CIN1, CIN2, and CIN3 grades of cervical intraepithelial neoplasia (CIN). The approach partitioned the epithelium into 10 segments. Image processing and machine vision algorithms were used to extract features from each segment. The features were then used to classify the segment and the result was fused to classify the whole epithelium. This research extends the previous research by dividing each of the 10 segments into 3 parts and uses a convolutional neural network to classify the 3 parts. The result is then fused to classify the segments and the whole epithelium. The experimental data consists of 65 images. The proposed method accuracy is 77.25% compared to 75.75% using the previous method for the same dataset. (C) 2017 The Authors. Published by Elsevier B.V.
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