Abstract
Abstract This paper has compared the performance of different color spaces of fundus images for automatic detection of exudates. A convolutional neural network was employed to assess the performances of different color spaces generated by orthogonal transformation of the original colors in red/green/blue (RGB) space. Experiments were conducted on two publicly available databases: (1) DIARETDB1 and (2) e-Ophtha. Based on the experimental results, this study has proposed a new color space of fundus images with three channels: (i) second eigenchannel of the RGB space, (ii) hue and (iii) saturation channels of Hue/Saturation and Intensity (HSI) space. This achieved an accuracy, sensitivity and specificity of 98.2%, 0.99 and 0.98, respectively. Twenty times 20-fold cross validation technique confirmed that proposed color space obtained higher replicability compared with conventional color spaces.
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