Feature-reconstructed diabetic retinopathy classification using variational autoencoder with disentanglement factor

Diabetic Retinopathy or DR is one of the leading causes of blindness in the working age population across the world, making its early detection and accurate classification as one of the major challenges in healthcare and medical imaging. Over the years various approaches have been developed like conventional deep learning models to classify DR or grade DR according to its severity. However, it has been observed that some of the major challenges encountered by various approaches is the lack of a lightweight feature focused learning mechanism. In this study, the authors propose a Variational Autoencoder (VAE) with disentanglement factor (Beta) and Logistic Regression based framework to classify DR in both ways, binary and multilevel classification. The proposed architecture aims to extract the fine features and textures of the retinal images compress it into a latent vector via the encoder and then expand the image map into a reconstructed image which aids the Logistic Regression classifier to classify the images and distinguish the severity of the disease. The proposed model was evaluated on two well-known datasets, APTOS 2019 dataset and DDR dataset. In case of binary classification, the model achieved an accuracy of 98.64% on the APTOS 2019 dataset and a 97.83% accuracy when evaluated on the DDR dataset. On multilevel classification, the model recorded an accuracy of 97.80% and 97.23% on APTOS 2019 dataset and DDR dataset respectively. These findings highlight the potential of the proposed method as an accurate and effective tool for automated DR screening and severity grading.