Deep Learning Ophthamology: Predicting Eye Diseases Using Pre-Trained Neural Network Algorithm

Authors- Siva Prasad J, Sathish Reddy Cm, Lakshmi Narayana U

Abstract-– Retinal imaging has emerged as a valuable tool in the early detection and diagnosis of various systemic diseases. This study presents a novel approach for the simultaneous prediction of multiple diseases utilizing retinal images. The proposed methodology involves the collection of a diverse dataset comprising retinal images labelled with the presence or absence of multiple diseases, including but not limited to diabetic retinopathy, age-related macular degeneration, glaucoma, and hypertensive retinopathy. Preprocessing techniques are applied to ensure data consistency and remove noise, followed by feature extraction from retinal images using advanced deep learning architectures. Machine learning models, including multi-label classification and multi-task learning, are trained on the extracted features to predict the presence of multiple diseases simultaneously. The performance of the models is evaluated using rigorous validation techniques, including accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC- ROC). Clinical validation is conducted to assess the effectiveness of the predictive system in real-world healthcare settings. The integration of the predictive system into clinical workflows is discussed, emphasizing seamless interaction with healthcare professionals and compliance with regulatory standards. The study concludes with insights into ongoing research and development efforts aimed at further improving the accuracy and scope ofmulti- disease prediction using retinal images using VGG16 framework in deep learning framework. This research represents a significant step towards leveraging retinal imaging for comprehensive disease diagnosis and management, with the potential to enhance early intervention and improve patient outcomes.

DOI: /10.61463/ijset.vol.13.issue2.303