Glandular structure-guided classification of microscopic colorectal images using deep learning

Awan, Ruqayya, Al-Maadeed, Somaya, Al-Saady, Rafif and Bouridane, Ahmed (2019) Glandular structure-guided classification of microscopic colorectal images using deep learning. Computers & Electrical Engineering. p. 106450. ISSN 0045-7906 (In Press)

[img] Text
manuscript accepted.pdf - Accepted Version
Restricted to Repository staff only until 23 August 2020.
Available under License Creative Commons Attribution Non-commercial No Derivatives 4.0.

Download (4MB) | Request a copy
Official URL: https://doi.org/10.1016/j.compeleceng.2019.106450

Abstract

In this work, we propose to automate the pre-cancerous tissue abnormality analysis by performing the classification of image patches using a novel two-stage convolutional neural network (CNN) based framework. Rather than training a model with features that may correlate among various classes, we propose to train a model using the features which vary across the different classes. Our framework processes the input image to locate the region of interest (glandular structures) and then feeds the processed image to a classification model for abnormality prediction. Our experiments show that our proposed approach improves the classification performance by up to 7% using CNNs and more than 10% while using texture descriptors. When testing with gland segmented images, our experiments reveal that the performance of our classification approach is dependent on the gland segmentation approach which is a key task in gland structure-guided classification.

Item Type: Article
Uncontrolled Keywords: Colorectal cancer, Glandular structures, Gland segmentation, Gland-guided classification, Deep learning
Subjects: G400 Computer Science
H600 Electronic and Electrical Engineering
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Elena Carlaw
Date Deposited: 05 Sep 2019 11:16
Last Modified: 11 Oct 2019 13:21
URI: http://nrl.northumbria.ac.uk/id/eprint/40531

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics


Policies: NRL Policies | NRL University Deposit Policy | NRL Deposit Licence