Prediction And Classification Of Glaucoma Disease Through GAN Based Efficient Deep Learning Framework

In modern days Glaucoma is become a disease that make impact on matter of visions for human eyesight. This is an irreversible disease where vision difficulties will be outcomes. Now ML oriented Learning models based framework is created for the proper identifying of glaucoma. So purpose of particular work of making a Computer Aided Diagnostic System may perform faster detection and diagnosis as well as providing quicker treatment to patients. So the framework is created for detection of glaucoma using CNN and ML based approaches using SVM model on augmented datasets. The classes of diseases are classified as glaucoma and non-glaucoma found out for this jobs. Here CNN is required for Image Classification of Glaucoma Positive or Glaucoma Negative, cases. So the use of manual image processing methods or human intervention based methods are not needed here . Here medical image segmentation, AI and ML based approach is used for performing medical diagnosis more accurately. So CNN based Deep neural network are giving more than 90% of accuracy of efficiently classifying the diseases. Here data augmentation provides a significant roles of generation of several more new images that are very much useful for this research work for effective disease identification system. Also our created model gives more than 90% of train/validation/test accuracy score for classification of augmented datasets for AI based GAN network of creating higher resolution images.