Diabetic macular oedema classification using gradient adaptive thresholding integrated active contour and ant lion spider monkey optimisation-based generative adversarial network

Abstract

Diabetic macular oedema (DME) is an eye disease, which can highly affect the visual activity for the diabetic patients. The imaging tool, optical coherence tomography (OCT) is used for diagnosis by the ophthalmologists for retinal disease identification. A novel gradient-based adaptive thresholding integrated active contour ant lion spider monkey optimisation driven general adversarial network (G-AT_AC+ALSMO-GAN) is introduced for the DME detection. Here, G-AT_AC scheme is applied for layer segmentation process. In addition, texture features, layer specific features, and image level features are mined for effective classification. The DME classification is carried out using GAN classifier, which is trained by developed ALSMO algorithm, which is the integration of the ant lion optimisation (ALO) and spider monkey optimisation (SMO). During the classification process, 12 layers and 13 boundaries are used for the segmentation process. The DME affected region is classified by the GAN classifier and the classified output is normal or affected one. The proposed method obtained the maximal accuracy, specificity and sensitivity of 94.12%, 92.77% and 98% respectively.