Ensembled Deep Convolutional Generative Adversarial Network for Grading Imbalanced Diabetic Retinopathy Recognition

Diabetic Retinopathy (DR) is one of the leading causes of blindness and vision loss worldwide. According to the International Diabetes Federation (IDF), approximately one-third of individuals with diabetes, equivalent to 32.2%, are affected by some form of DR. Due to the uneven data distribution, intra-class variance, and a dearth of ophthalmologists, DR diagnosis is considered challenging. In recent years, Convolutional Neural Networks (CNN) and supervised learning techniques have been potentially useful in computer vision applications. However, unsupervised CNN has received less attention. Moreover, it is more manageable to use synthetic images for model training with the recent advancements in graphics. Therefore, the proposed method combines the actual and augmented views using the Deep Convolutional Generative Adversarial Network (DCGAN) algorithm. The generated views are implemented to balance the minority class in the imbalanced dataset. Furthermore, a novel ensemble convolutional neural network algorithm named Different View Ensemble (DVE) that merges the weighted average prediction of CNN, CNN-i, and CNN+i algorithms has been proposed. The proposed algorithm is evaluated on the DDR and EyePACS datasets, and its performance is compared with K-Means, Fuzzy C-Means (FCM), and Autoencoder-based Deep Embedded Clustering Techniques (DEC). The results demonstrate the superiority of the proposed algorithm, achieving an accuracy rate of 97.4%, specificity of 99.6%, and sensitivity of 92.3%. The promising results underscore the potential impact of this methodology in enhancing the accuracy and reliability of automated diagnostic systems in the field of ophthalmology. Notably, the evaluation considers imbalanced data and a DCGAN-balanced dataset, where the proposed approach exhibits even better performance with balanced classes.