An Efficient Framework for Automated Screening of Clinically Significant
Macular Edema
- URL: http://arxiv.org/abs/2001.07002v1
- Date: Mon, 20 Jan 2020 07:34:13 GMT
- Title: An Efficient Framework for Automated Screening of Clinically Significant
Macular Edema
- Authors: Renoh Johnson Chalakkal, Faizal Hafiz, Waleed Abdulla, and Akshya
Swain
- Abstract summary: The present study proposes a new approach to automated screening of Clinically Significant Macular Edema (CSME)
The proposed approach combines 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.
- Score: 0.41998444721319206
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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