An approach to classification of data with highly localized unmarked features using neural networks
DOI:
https://doi.org/10.7494/csci.2019.20.3.3343Keywords:
saliency map, feature extraction, localized featuresAbstract
To face the increasing demand on quality healthcare, cutting edge automation technology is being applied in such demanding areas as medical imaging. This paper proposes a novel approach to classification problems on datasets with sparse, highly localized features. It is based on the use of saliency map in amplification of features. Unlike previous efforts, this approach does not use any prior information about feature localization. We present an experimental study based on Diabetic Retinopathy classification problem, in which our method has shown to achieve over 20\% higher accuracy in solving a two-class Diabetic Retinopathy classification problem than a naive approach based solely on residual neural networks. The dataset consists of 35120 images of various quality, inconsistent resolution and aspect ratio.Downloads
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