SODAS: Second-order optimization differential architecture search for diabetic retinopathy prediction.

Diabetic retinopathy, which is a retinal disease that results from diabetes, has become the leading cause of blindness. Early diagnose of diabetic retinopathy is crucial for preserving vision and saving lives. To date, several pioneering methods have been proposed for diagnosing diabetic retinopathy and have achieved preliminary results. However, several limitations persist, including the inability to detect diabetic retinopathy at various scales and low accuracy in large-scale real scenarios. Therefore, in this paper, we propose second-order optimization differential architecture search (SODAS) for predicting diabetic retinopathy. Firstly, we utilize neural architecture search to grade diabetic retinopathy to overcome the limitations of neglecting retinopathy at various scales caused by manually designing networks. Then, we integrate Gumbel-Softmax sampling into neural architecture search to encompass all the operation information during normalization, with the aims of reducing gradient information loss and improving accuracy. Additionally, we design second-order optimization to mitigate slow convergence associated with classical neural architecture search, which has been proven to converge quickly by detailed analysis and extensive results. Experimental results on benchmark datasets show that our SODAS has achieved average improvements of 12.1 %, 21.6 %, 28.8 %, 24.1 %, and 22.5 % in terms of accuracy, Cohen's kappa, AUC, IBA, and F1-score, respectively.