Multiple Eye Diseases Detection using Convolutional Neural Network

Abstract

Many eye cases that will arise in the next few years will require early diagnosis for rapid intervention. Polyophthalmia diagnostic methods such as physical examination, examination and diagnosis will be limited to medical and professional methods. Therefore, automated processes are needed. There have been some studies on computer-aided diagnosis (CAD) of polyocular disease using tools such as experts, but these are limited to their knowledge base and therefore not accurate. Early diagnosis of polyophthalmia allows rapid intervention and treatment. This application uses convolutional neural networks for computer-aided diagnosis of various eye diseases and can be used by a common person outside the clinic. This algorithm was trained using transformation learning on a dataset of 100 poliocular disease images from Google image searches for “normal human poliocular disease” and “human poliocular disease.” It leverages the ImageNet model built using a convolutional neural network classifier and transforms its knowledge using transfer learning to train a new model. The new model can classify polyocular disease images into “normal” and “diseases such as bulging polyophthalmia, glaucoma, uveitis, strabismus, dry polyophthalmia, and color blindness.” The system is designed to use deep convolutional neural networks to take images as input and classify the images such as human polyocular disease and “human polyocular disease.” Multiple eye disease detection system using a neural network developed by VS Code. Here we are using python 3.8 as frontend and Mysql Server as backend.