Lightweight MobileNet-Based Retinal Disease Classification Using OCT Images

Retinal diseases, such as Diabetic Macular Edema (DME), Choroidal Neovascularization (CNV), and drusen, are the leading causes of irreversible vision loss worldwide. Early and accurate diagnosis is critical for effective treatment and preservation of vision. Optical Coherence Tomography (OCT) has emerged as a standard, noninvasive imaging modality that provides high-resolution, cross-sectional images of the retina, en-abling detailed morphological assessment. However, the manual interpretation of a large volume of OCT scans is time-consuming, subjective, and requires significant expertise, leading to poten-tial diagnostic delays and inter-observer variability. This study presents an automated diagnostic system using deep learning to classify retinal OCT images. We propose a Convolutional Neural Network (CNN)-based model designed to accurately and efficiently detect the presence of common retinal pathologies. The model was trained and validated on a publicly available dataset of retinal OCT images categorized into four classes: Normal, CNV, DME, and Drusen. This automated system has the potential to serve as a powerful decision support tool, streamline the diag-nostic workflow, facilitate large-scale screening programs, and ultimately improve patient outcomes through timely intervention.