New Deep Neural Nets for Fine-Grained Diabetic Retinopathy Recognition on Hybrid Color Space

Automatic diabetes retinopathy (DR) recognition can help DR carriers to receive treatment in early stages and avoid the risk of vision loss. In this paper, we emphasize the role of multiple filter sizes in learning fine-grained discriminant features and propose: (i) two deep convolutional neural networks - Combined Kernels with Multiple Losses Network (CKML Net) and VGGNet with Extra Kernel (VNXK), which are an improvement upon GoogLeNet and VGGNet in context of DR tasks. Learning from existing research, (ii) we propose a hybrid color space, LGI, for DR recognition via proposed nets. (iii) Transfer learning is applied to solve the challenge of imbalanced dataset. The effectiveness of proposed new nets and color space is evaluated using two grand challenge retina datasets: EyePACS and Messidor. Our experimental results show: (iv) CKML Net improves upon GoogLeNet and VNXK improves upon VGGNet on both datasets using the LGI color space. Additionally, proposed methodology improves upon other state of the art results on Messidor dataset for referable/non-referable screening.

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