dc.contributor.author |
Chalakkal, Renoh |
en |
dc.contributor.author |
Hafiz, Faizal |
en |
dc.contributor.author |
Abdulla, Waleed |
en |
dc.contributor.author |
Swain, Akshya |
en |
dc.date.accessioned |
2020-04-09T00:24:39Z |
en |
dc.date.issued |
2020-01-20 |
en |
dc.identifier.citation |
Arxiv (2001.07002v1). 20 Jan 2020. 9 pages |
en |
dc.identifier.uri |
http://hdl.handle.net/2292/50298 |
en |
dc.description.abstract |
The present study proposes a new approach to automated screening of Clinically Significant Macular Edema (CSME) and addresses two major challenges associated with such screenings, i.e., exudate segmentation and imbalanced datasets. The proposed approach replaces the conventional exudate segmentation based feature extraction by combining a pre-trained deep neural network with meta-heuristic feature selection. A feature space over-sampling technique is being used to overcome the effects of skewed datasets and the screening is accomplished by a k-NN based classifier. The role of each data-processing step (e.g., class balancing, feature selection) and the effects of limiting the region-of-interest to fovea on the classification performance are critically analyzed. Finally, the selection and implication of operating point on Receiver Operating Characteristic curve are discussed. The results of this study convincingly demonstrate that by following these fundamental practices of machine learning, a basic k-NN based classifier could effectively accomplish the CSME screening. |
en |
dc.relation.ispartof |
Arxiv |
en |
dc.rights |
Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher. |
en |
dc.rights.uri |
https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm |
en |
dc.rights.uri |
http://arxiv.org/licenses/nonexclusive-distrib/1.0/ |
en |
dc.subject |
eess.IV |
en |
dc.subject |
eess.IV |
en |
dc.subject |
cs.CV |
en |
dc.subject |
cs.LG |
en |
dc.subject |
cs.NE |
en |
dc.title |
An Efficient Framework for Automated Screening of Clinically Significant Macular Edema |
en |
dc.type |
Report |
en |
dc.rights.holder |
Copyright: The authors |
en |
pubs.author-url |
http://arxiv.org/abs/2001.07002v1 |
en |
dc.rights.accessrights |
http://purl.org/eprint/accessRights/OpenAccess |
en |
pubs.subtype |
Working Paper |
en |
pubs.elements-id |
793284 |
en |
pubs.org-id |
Engineering |
en |
pubs.org-id |
Department of Electrical, Computer and Software Engineering |
en |
pubs.org-id |
Science |
en |
pubs.org-id |
Physics |
en |
pubs.arxiv-id |
2001.07002 |
en |
pubs.number |
2001.07002v1 |
en |
pubs.record-created-at-source-date |
2020-04-09 |
en |