Eyes Disease Detection Using Deep Learning Methodologies

Our eyes are fundamental part of our daily lives, enabling us to engage and interact with the surroundings. Our eyes serve as vital sensory organs, providing us with a wealth of information about the surrounding world. The growing occurrence of eye diseases resulting in vision impairment and blindness highlights the vital importance of promptly identifying and diagnosing these conditions to facilitate efficient therapeutic interventions. This research utilizes Convolutional-Neural-Networks (CNNs) to scrutinize images, extracting distinctive features that enable precise identification of diverse eye conditions. Recent progress in the field of CNNs has yielded promising results, showcasing their efficacy in identifying and diagnosing diverse eye conditions including bulging eyes, crossed eyes, uveitis, glaucoma, and cataracts. The utilization of Convolutional Neural Networks (CNNs) for detecting eye illnesses can enhance the precision, velocity, and effectiveness of diagnostic processes. This advancement enables earlier intervention and contributes to more favorable outcomes for patients. The remarkable precision exhibited by the model in diagnosing eye diseases underscores the effectiveness of incorporating transfer learning, specifically with pre-trained models like ResNet50. Employing CNNs in conjunction with transfer learning, particularly utilizing models such as ResNet50, has emerged as a successful strategy for identifying and diagnosing various eye conditions. This approach not only contributes to improved patient outcomes but also helps prevent the progression of more severe vision problems by accurately detecting and diagnosing eye illnesses.