INTERNATIONAL JOURNAL OF RESEARCH CULTURE SOCIETY

Abstract: The human eye is a complex organ where minute anatomical changes can signal systemic health conditions, making it a vital window for non-invasive diagnosis. The field of ophthalmic imaging has been revolutionized by the advent of high-resolution digital modalities and sophisticated computational techniques. This paper provides a comprehensive overview of contemporary technologies and methods for processing images of the human eye. We begin with a literature survey charting the evolution from manual clinical assessment to AI-driven diagnostic systems. The core technologies are classified into advanced imaging modalities, such as Optical Coherence Tomography (OCT) and Fundus Autofluorescence, and computational techniques, predominantly encompassing traditional image processing and modern deep learning (DL) architectures. A comparative analysis reveals that while traditional algorithms are robust for specific, well-defined tasks, deep learning models, particularly Convolutional Neural Networks (CNNs), offer superior performance for complex classification and segmentation challenges, albeit with a need for large, annotated datasets. The discussion critically addresses key challenges, including data scarcity, model interpretability ("black box" problem), and integration into clinical workflows. We conclude that the future of ocular image processing lies in multimodal data fusion, the development of explainable AI, and the creation of robust, generalizable models that can function as reliable clinical decision-support tools, ultimately enhancing early detection and personalized treatment of ocular and systemic diseases.