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

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 utilizing 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 quality of the synthetic images as a whole and capturing finer details is the main goal. The goal of this effort is to improve the accuracy and authenticity of synthetic fundus images, leading to new developments in fundus image synthesis. Initially, a raw dataset has been preprocessed using the Gaussian filtering technique, which helps to minimize the unnecessary noise in the images. Then, a GAN is used to balance out the dataset, which helps to produce synthetic images and produce 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%.