# Transfer Learning for Automated OCTA Detection of Diabetic Retinopathy

arXiv: https://arxiv.org/abs/1910.01796 · HTML: https://mlsanity.com/paper/1910.01796/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Oct 2019.

## Built on by (most cited first, 8 of 8)

- [BSDA-Net: A Boundary Shape and Distance Aware Joint Learning Framework for Segmenting and Classifying OCTA Images](https://mlsanity.com/paper/2107.04823/) (2021)
- [Contrastive learning-based pretraining improves representation and transferability of diabetic retinopathy classification models](https://mlsanity.com/paper/2208.11563/) (2022)
- [DISCOVER: 2-D Multiview Summarization of Optical Coherence Tomography Angiography for Automatic Diabetic Retinopathy Diagnosis](https://mlsanity.com/paper/2401.05137/) (2024)
- [An Ensemble Method to Automatically Grade Diabetic Retinopathy with Optical Coherence Tomography Angiography Images](https://mlsanity.com/paper/2212.06265/) (2022)
- [OCT-SelfNet: A Self-Supervised Framework with Multi-Modal Datasets for Generalized and Robust Retinal Disease Detection](https://mlsanity.com/paper/2401.12344/) (2024)
- [Multi-OCT-SelfNet: Integrating Self-Supervised Learning with Multi-Source Data Fusion for Enhanced Multi-Class Retinal Disease Classification](https://mlsanity.com/paper/2409.11375/) (2024)
- [Rapid Classification of Glaucomatous Fundus Images](https://mlsanity.com/paper/2102.04400/) (2021)
- [Interpretable Diabetic Retinopathy Diagnosis based on Biomarker Activation Map](https://mlsanity.com/paper/2212.06299/) (2022)

## Abstract (arXiv)

Purpose: To test the feasibility of using deep learning for optical coherence tomography angiography (OCTA) detection of diabetic retinopathy (DR). Methods: A deep learning convolutional neural network (CNN) architecture VGG16 was employed for this study. A transfer learning process was implemented to re-train the CNN for robust OCTA classification. In order to demonstrate the feasibility of using this method for artificial intelligence (AI) screening of DR in clinical environments, the re-trained CNN was incorporated into a custom developed GUI platform which can be readily operated by ophthalmic personnel. Results: With last nine layers re-trained, CNN architecture achieved the best performance for automated OCTA classification. The overall accuracy of the re-trained classifier for differentiating healthy, NoDR, and NPDR was 87.27%, with 83.76% sensitivity and 90.82% specificity. The AUC metrics for binary classification of healthy, NoDR and DR were 0.97, 0.98 and 0.97, respectively. The GUI platform enabled easy validation of the method for AI screening of DR in a clinical environment. Conclusion: With a transfer leaning process to adopt the early layers for simple feature analysis and to re-train the upper layers for fine feature analysis, the CNN architecture VGG16 can be used for robust OCTA classification of healthy, NoDR, and NPDR eyes. Translational Relevance: OCTA can capture microvascular changes in early DR. A transfer learning process enables robust implementation of convolutional neural network (CNN) for automated OCTA classification of DR.