MobileP²VT: Parallel Hybrid Structure makes Lightweight Network Stronger for Diabetic Retinopathy Classification

The emergence of lightweight models provides a new solution for the classification of Diabetic Retinopathy (DR). These lightweight models have a fewer parameters and low computational complexity. However, existing lightweight models in Diabetic Retinopathy (DR) still face challenges: Convolutional Neural Networks (CNNs) are limited by their receptive fields and lack modeling capabilities over long distances. Although the models based on Transformer can capture global information, the usually have high computational complexity. Recent lightweight models that try to combine the strengths of CNN and Transformer tend to have a simple serial architecture that fails to take full advantage of both. To address these challenges, we propose a new lightweight network, MobileP<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>VT, for the classification of diabetic retinopathy. The model uses lightweight convolution and Transformer parallel processing to capture local details and global information of the image. In addition, we have designed a new attention mechanism, Sparse Token Attention (SPTA), which reduces the amount of attention computation in Transformer. Experimental results on Eyepacs and APTOS 2019 datasets demonstrate the high efficiency of MobileP<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>VT.