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Smart Diagnosis System for Retinal Diseases using OCT Imaging

Dr.R.Jegadeesan,Dr.V.Neelima,Nikhil Kumar Rajak,Syed Mustafa Ayaan Ali, Ragi Sai Saketh,Ranjith Reddy

Department of Computer Science and Engineering Jyothishmathi Institute of Technology and Science, India Email: syedali040205@gmail.com

Abstract—Optical Coherence Tomography (OCT) is a widely used non-invasive imaging modality that enables detailed visual-ization of retinal microstructures and supports early diagnosis of retinal diseases. However, manual interpretation of OCT scans is time-consuming, subjective, and highly dependent on clinical ex-pertise, which limits scalability in real-world healthcare settings. Although deep learning-based approaches have demonstratedstrong performance in automated retinal disease classification,  most existing systems lack interpretability, clinical workflow integration, and automated diagnostic reporting. This paper presents a comprehensive Smart OCT-based Reti-nal Disease Diagnosis System that integrates ensemble deep learn-ing with AI-driven intelligent agents to enhance clinical usability.An ensemble model combining EfficientNetB0 and VGG-16 is employed to perform eight-class retinal disease classification,including Age-related Macular Degeneration (AMD), Choroidal Neovascularization (CNV), Central Serous Retinopathy (CSR),Diabetic Macular Edema (DME), Diabetic Retinopathy (DR),Drusen, Macular Hole (MH), and Normal retina. Beyond classi-fication, the system incorporates LangGraph-based AI agents to  automatically generate diagnostic reports, provide visual explana-tions using Grad-CAM, and produce patient-friendly summaries.The proposed framework achieves 95.6% classification accu-racy while addressing key limitations of existing OCT-based di-agnostic systems through improved interpretability, automation,and scalability. Extensive experimental validationdemonstratesthe system’s capability to assist ophthalmologists in making fasterand more accurate diagnostic decisions.
Index Terms—Optical Coherence Tomography, Retinal DiseaseClassification, Deep Learning, EfficientNet, VGG-16, Ensem-ble Learning, Explainable AI, LangGraph, Medical Imaging,Computer-Aided Diagnosis