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

Authors: Shweta Reddy; Shridevi Soma

Addresses: BLDEA's V.P. Dr. P.G. Halakatti College of Engineering, Vijayapure, India ' Poojya Doddappa Appa College of Engineering, Kalaburagi, India

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.

Keywords: macular oedema classification; generative adversarial network; ant lion optimisation algorithm; spider monkey optimisation algorithm; gradient active contour; optical coherence tomography image.

DOI: 10.1504/IJBIC.2022.128098

International Journal of Bio-Inspired Computation, 2022 Vol.20 No.4, pp.241 - 255

Received: 09 Oct 2021
Received in revised form: 11 Jun 2022
Accepted: 19 Jun 2022
Published online: 05 Jan 2023 *

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