ANALYSIS AND DETECTION OF EYE DISEASES USING DEEP LEARNING METHEDOLOGYE

Abstract

Our eyes play an important part in our daily lives, allowing us to navigate and interact with our surroundings. They offer us a plethora of information about the world around us as critical sensory organs. However, the prevalence of eye illnesses, which cause vision loss and blindness, is increasing. Early detection and diagnosis are critical for efficient treatment and management of eye illnesses. This paper uses Convolutional neural networks (CNNs) to extract features from images and effectively identify them. CNNs have demonstrated encouraging results in identifying and diagnosing a variety of eye illnesses in recent years, including bulging eyes, Crossed eyes, Uveitis, glaucoma, and cataracts. The application of CNNs in eye illness detection can enhance diagnosis accuracy, speed, and efficiency, allowing for earlier intervention and better patient outcomes. The model's excellent accuracy in diagnosing eye diseases highlights the utility of using transfer learning with pre-trained models such as ResNet50. The use of CNNs and transfer learning with models such as ResNet50 has proven to be an effective technique in the identification and diagnosis of eye diseases. The model can enhance patient outcomes and avoid the development of more severe vision issues by precisely recognizing and diagnosing eye illnesses.