DIABETIC MACULAR EDEMA CLASSIFICATION WITH OCT IMAGES USING GENERATIVE ADVERSARIAL NETWORK AND ACTIVE CONTOUR MODEL

Abstract

The major reason for blindness is diabetic macular edema (DME) and hence detection of DME at early stage using optical coherence tomography (OCT) is commonly employed for diagnosing retinal diseases. An accurate disease identification and classification poses a challenging task due to the difficulty in differentiating the abnormal and healthy regions. To overcome these issues and to accurately classify the DME, an effective DME classification approach named antlion spider monkey optimization-based generative adversarial network (ALSMO-based GAN) is proposed in this research for segmenting the retinal layers and to classify the DME more accurately. With the generator and the discriminator components of GAN, the DME is effectively classified so that the devised ALSMO algorithm can be used to train the process of GAN. The inspiration of the foraging and the hunting behavior enable the optimization to increase the rate of convergence and to achieve global optimal solution by reducing the local optima. With the segmented retinal layer, the classification process is progressed through the extraction of relevant features from the retinal layers. The performance of the developed method is verified using measures like accuracy, sensitivity, and specificity which attained values of 92.5%, 98%, and 92.3%, respectively.