# Optic-Net: A Novel Convolutional Neural Network for Diagnosis of Retinal Diseases from Optical Tomography Images

arXiv: https://arxiv.org/abs/1910.05672 · HTML: https://mlsanity.com/paper/1910.05672/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Oct 2019.

## Built on by (most cited first, 15 of 15)

- [MedImageInsight: An Open-Source Embedding Model for General Domain Medical Imaging](https://mlsanity.com/paper/2410.06542/) (2024)
- [VTGAN: Semi-supervised Retinal Image Synthesis and Disease Prediction using Vision Transformers](https://mlsanity.com/paper/2104.06757/) (2021)
- [RV-GAN: Segmenting Retinal Vascular Structure in Fundus Photographs using a Novel Multi-scale Generative Adversarial Network](https://mlsanity.com/paper/2101.00535/) (2021)
- [Inflation of test accuracy due to data leakage in deep learning-based classification of OCT images](https://mlsanity.com/paper/2202.12267/) (2022)
- [To Erase, or Not to Erase: Robust Training-Free Concept Erasure with Preservation aware Adaptive Ranked Subspace Expansion](https://mlsanity.com/paper/2607.23492/) (2026)
- [Medical Image Deidentification, Cleaning and Compression Using Pylogik](https://mlsanity.com/paper/2304.12322/) (2023)
- [From Redaction to Restoration: Deep Learning for Medical Image Anonymization and Reconstruction](https://mlsanity.com/paper/2604.11376/) (2026)
- [OracleZoom: On-Policy Self-Distillation Inspired Reference-Constrained Recursive Image Super Resolution](https://mlsanity.com/paper/2609.06490/) (2026)
- [BEFORE THE FLIP: Measuring Hidden Score Shifts In Quantized Vision Language Models Before The Answer Changes for Visual Question Answering](https://mlsanity.com/paper/2609.06922/) (2026)
- [Mitigating domain shift in AI-based tuberculosis screening with unsupervised domain adaptation](https://mlsanity.com/paper/2111.04893/) (2021)
- [Weakly-supervised Generative Adversarial Networks for medical image classification](https://mlsanity.com/paper/2111.14605/) (2021)
- [Feature Representation Learning for Robust Retinal Disease Detection from Optical Coherence Tomography Images](https://mlsanity.com/paper/2206.12136/) (2022)
- [Opto-UNet: Optimized UNet for Segmentation of Varicose Veins in Optical Coherence Tomography](https://mlsanity.com/paper/2302.14808/) (2023)
- [Leveraging Complementary Attention maps in vision transformers for OCT image analysis](https://mlsanity.com/paper/2310.14005/) (2023)
- [New Evidence, Same Choice: Testing Physical Experiment Selection in Vision Language Models](https://mlsanity.com/paper/2609.11022/) (2026)

## Abstract (arXiv)

Diagnosing different retinal diseases from Spectral Domain Optical Coherence Tomography (SD-OCT) images is a challenging task. Different automated approaches such as image processing, machine learning and deep learning algorithms have been used for early detection and diagnosis of retinal diseases. Unfortunately, these are prone to error and computational inefficiency, which requires further intervention from human experts. In this paper, we propose a novel convolution neural network architecture to successfully distinguish between different degeneration of retinal layers and their underlying causes. The proposed novel architecture outperforms other classification models while addressing the issue of gradient explosion. Our approach reaches near perfect accuracy of 99.8% and 100% for two separately available Retinal SD-OCT data-set respectively. Additionally, our architecture predicts retinal diseases in real time while outperforming human diagnosticians.