Diabetic retinopathy is a common complication of diabetes, that affects blood vessels in the light-sensitive tissue called the retina. It is the most common cause of vision loss among people with diabetes and the leading cause of vision impairment and blindness among working-age adults. Recent progress in the use of automated systems for diabetic retinopathy diagnostics has offered new challenges for the industry, namely the search for a less resource-intensive architecture, e.g., for the development of low-cost embedded software. This paper proposes a comparison between two widely used conventional architectures (DenseNet, ResNet) with the new optimized one (EfficientNet). The proposed methods classify the retinal image as one of 5 class cases based on the dataset obtained from the 4th Asia Pacific Tele-Ophthalmology Society (APTOS) Symposium.

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An article represents the results of the experiments constructing of conventional anaphora resolution system for gender-balanced corpora GAP. The main idea behind the conducted set of experiments is taking advantage over the kernel trick mechanism of deep word vectorization for various semantic and syntax features. We will show that it has a positive impact on system performance versus the traditional approach.