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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Pathan, Shafi
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Computer Science & IT Engineering

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Structured data collection and deep learning for retinal OCT image-to-text translation: a comprehensive framework Mande, Uday; Pathan, Shafi; Chandre, Pankaj; Mande, Sharvari
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i2.pp1050-1061

Abstract

This paper presents a comprehensive framework for structured data collection and deep learning (DL)-based translation of retinal optical coherence tomography (OCT) images into diagnostic text. The suggested approach guarantees high-quality OCT data for model training through the use of sophisticated image processing methods like edge detection, noise suppression, and contrast improvement. The study utilizes 84,484 retinal images from the OCT dataset available on Kaggle. The research utilizes various preprocessing techniques, such as median and Gaussian filtering, along with data augmentation strategies like translation, rotation, and scaling, to mitigate class imbalances and improve model performance. The system automatically identifies and categorizes retinal diseases such as drusen, diabetic macular edema (DME), and choroidal neovascularization (CNV) by integrating feature extraction and selection with DL techniques. The research highlights the importance of effective data handling and model scalability to address the increasing need for automated diagnostic tools in ophthalmology. This framework aims to support ophthalmologists in managing the increasing incidence of diabetic retinopathy (DR) and other retinal conditions by enhancing the efficiency of retinal image analysis, thereby improving patient results through early detection and treatment.
Co-Authors Chandre, Pankaj Mande, Sharvari Mande, Uday