Hinge attention network: A joint model for diabetic retinopathy severity grading

Diabetic Retinopathy is one of the prominent reasons for permanent blindness in working age, long term diabetic patients. With the prevalence in raise of diabetics, majority of the people are endangered to permanent vision loss. The advancements in medical imaging techniques enabled the research community to focus on developing automated and computerized systems for diagnosing retinopathy in early stages. But, it is a very complex challenge due to the presence of high intra-class variations and imbalanced data distribution for higher grades of severity. In recent years, various deep learning based models have been designed for automating the process of retinopathy severity classification. In this research work, we present a fascinating deep learning model with multiple attention stages called Hinge Attention Network (HA-Net). Proposed model consists of a pre-trained VGG16 base to extract initial spatial representation from retinal scan images, spatial attention autoencoder to learn lesion specific latent representations in spatial dimensions and a channel attention based hinge neural network to grab category based discriminative features in channel dimension and classify the severity grade of retinopathy. In addition to spatial and channel attention mechanism, we use Convolutional LSTM layer to prioritize highly important spatial maps before passing to hinge neural network. All these components of HA-Net, enabled it to make generalised and accurate predictions on unseen data. The effectiveness and acceptability of proposed model is proved by validating it using two benchmark datasets, Kaggle APTOS 2019 and ISBI IDRiD. Extensive experimental studies on these datasets reveal that, proposed HA-Net outstrip several existing models by achieving an accuracy of 85.54% on Kaggle APTOS, and an accuracy of 66.41% on IDRiD datasets.