Non-proliferative diabetic retinopathy detection using Rosmarus Quagga optimized explainable generative meta learning based deep convolutional neural network model.

purposeNon-Proliferative Diabetic Retinopathy (NPDR) is a complication of diabetes disease where there is damage of the blood vessels in retina but with no signs of formation of new vessels. It is present in the earlier stages and therefore the control of diabetes combined with constant check-up can address the challenge. Existing models face several challenges such as heterogeneity of the lesion with regard to size, shape, and distribution. Therefore, to reduce those existing challenges, in this research, a novel model Rosmarus Quagga optimized Explainable generative Meta learning based Deep Convolutional Neural Network (RQ-EGMCN) is proposed for Non-Proliferative Diabetic Retinopathy. The main purpose of the proposed research is to develop and validate the effective diagnosis of severe DR with lesion recognition using the retinal images.

methodsThe presented approach develops the Rosmarus Quagga optimization, which exhibits the adaptive foraging behaviors are integrated along with the aspects of the leader-based feeding strategies to enhance the detection accuracy. Simultaneously, the proposed model employs explainable Convolutional Neural Networks to ensure interpretations which in turn provides a tradition of decision making by presenting the attention and saliency maps. The generative component allows to generate realistic retinal images for training purposes and meta-learning, when applied to new data, and accelerates learning while enhancing its' generalization potential.

main outcome measuresMoreover, the proposed model improves the NPDR diagnosis by minimizing the computational complexity, improving the accuracy and versatility of the model across different datasets.

resultsExperimental analysis show that the RQ-EGMCN model obtained the maximum accuracy, precision, and recall of 95.47%, 95.34%, and 95.24% for the diabetic retinopathy detection dataset, respectively.