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.

Meeting Name

Complex Adaptive Systems Conference with Theme: Engineering Cyber Physical Systems, CAS (2017: Oct. 30-Nov. 1, Chicago, IL)


Electrical and Computer Engineering

Keywords and Phrases

Adaptive systems; Artificial intelligence; Complex networks; Convolution; Data fusion; Decision support systems; Diseases; Embedded systems; Image classification; Image processing; Neural networks; Cervical cancers; Cervical intraepithelial neoplasias; Classifcation; Clinical decision support systems; Convolutional neural network; Histology images; Machine vision algorithm; Uterine cervical cancer; Image segmentation; Convolutoin Neural Networks; Image classifcation

International Standard Serial Number (ISSN)


Document Type

Article - Conference proceedings

Document Version

Final Version

File Type





© 2017 Elsevier, All rights reserved.

Creative Commons Licensing

Creative Commons License
This work is licensed under a Creative Commons Attribution-No Derivative Works 4.0 License.

Publication Date

01 Oct 2017