Convolutional Neural Network Based Localized Classification of Uterine Cervical Cancer Digital Histology Images

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 algor...

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Bibliographic Details
Published in:Procedia computer science 2017, Vol.114, p.281-287
Main Authors: Almubarak, Haidar A., Stanley, R. Joe, Long, Rodney, Antani, Sameer, Thoma, George, Zuna, Rosemary, Frazier, Shelliane R.
Format: Article
Language:English
Subjects:
Cervical Cancer
Clinical Decision Support Systems
Convolutoin Neural Networks
Data Fusion
Image classifcation
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Summary: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.
ISSN:1877-0509
1877-0509
DOI:10.1016/j.procs.2017.09.044