Towards Accurate Glaucoma Identification: GAN-Enhanced Synthesis and Classification Using Pretrained MobileNetV2

Abstract

Irreversible vision loss, which often develops slowly and with no outward signs of illness, is most commonly caused by glaucoma. Because it may slow the disease's progression, the initial stages of glaucoma detection are of the utmost importance. Ordinary procedures and manual assessments are based on traditional diagnostic techniques, which are notoriously imprecise. It follows that automated glaucoma analysis is critically important for the early and precise detection of glaucoma.  Also, on the other hand, the medical image dataset is mostly imbalanced in nature.  To overcome all these issues, the present research work developed an effective framework by Utilising Generative Adversarial Networks (GAN) to synthesize images to balance out the dataset.  For example, when dealing with fundus images, conventional methods, such as translation from image-to-image operations, are used. In particular, these techniques are employed to produce synthetic fundus images and the associated vessel networks. Improving the synthetic images' quality as a whole and capturing finer details is the main goal. The goal of this effort is to improve synthetic fundus images in terms of accuracy and authenticity, which will lead to new developments in the area of fundus image synthesis.  Initially, a raw dataset has been preprocessed using the Gaussian filtering technique, which helps to minimize the unnecessary noise over the images.  Then GAN is used to balance out the dataset, which helps to produce synthetic images and produces reliable outcomes in classification tasks.  The next segmenting optic cup is done using the Enhanced Level Set Algorithm.  Finally, Pretrained MobileNetV2 is used for the accurate classification of glaucomatous images into normal and abnormal.  Experimental results show that our proposed frameworks perform well compared to existing approaches with an accuracy of 98.9%.