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Early diagnosis of retinal diseases such as diabetic retinopathy has had theattention of many researchers. Deep learning through the introduction ofconvolutional neural networks has become a prominent solution for image-relatedtasks such as classification and segmentation. Most tasks in imageclassification are handled by deep CNNs pretrained and evaluated on imagenetdataset. However, these models do not always translate to the best result onother datasets. Devising a neural network manually from scratch based onheuristics may not lead to an optimal model as there are numeroushyperparameters in play. In this paper, we use two nature-inspired swarmalgorithms: particle swarm optimization (PSO) and ant colony optimization (ACO)to obtain TDCN models to perform classification of fundus images into severityclasses. The power of swarm algorithms is used to search for variouscombinations of convolutional, pooling, and normalization layers to provide thebest model for the task. It is observed that TDCN-PSO outperforms imagenetmodels and existing literature, while TDCN-ACO achieves faster architecturesearch. The best TDCN model achieves an accuracy of 90.3%, AUC ROC of 0.956,and a Cohen kappa score of 0.967. The results were compared with the previousstudies to show that the proposed TDCN models exhibit superior performance.

Early Diagnosis of Retinal Blood Vessel Damage via Deep Learning-Powered Collective Intelligence Models