Detection and Classification of Eye Disease using Deep Learning Algorithms

Abstract

The early detection and management of multiple diseases are critical for improving patient outcomes, particularly in healthcare fields like ophthalmology. This paper proposes a novel approach to the prediction and classification of multiple diseases using ocular images, with an emphasis on determining the severity of these conditions. With the advancement of artificial intelligence (AI) and machine learning (ML), the integration of deep learning models has shown promising results in analyzing medical images for various disease detection tasks. The proposed methodology involves the use of ocular images, such as fundus images, OCT scans, and retinal photographs, to detect and classify a range of ocular diseases like diabetic retinopathy, glaucoma, age-related macular degeneration, and cataracts. By leveraging convolutional neural networks (CNNs) and other advanced machine learning algorithms, the system is trained on a large dataset of labeled ocular images. The model learns to identify key features in the images that are indicative of different diseases. Beyond classification, the system also incorporates severity analysis by assessing the degree of damage or progression of each condition. This is achieved by utilizing image segmentation techniques and quantitative measures such as lesion size, shape, and location, which are critical for evaluating the severity of the disease. The final output provides both the diagnosis and a severity score, enabling clinicians to prioritize treatment and interventions. The proposed system not only aims to assist ophthalmologists in routine screenings but also strives to improve accessibility to healthcare by enabling remote disease detection. By utilizing ocular images as a non-invasive diagnostic tool, this approach promises to enhance the efficiency and accuracy of multi-disease diagnosis, potentially leading to better patient care and outcomes in ophthalmology and beyond.