# DRDrV3: Complete Lesion Detection in Fundus Images Using Mask R-CNN, Transfer Learning, and LSTM

arXiv: https://arxiv.org/abs/2108.08095 · HTML: https://mlsanity.com/paper/2108.08095/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 25 references that are in the map.
- First posted on arXiv 18 Aug 2021.

## Builds on (most cited first, 9 of 9)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [A Brief Review of Domain Adaptation](https://mlsanity.com/paper/2010.03978/) (2020)
- [DeepMSRF: A novel Deep Multimodal Speaker Recognition framework with Feature selection](https://mlsanity.com/paper/2007.06809/) (2020)
- [DRDr II: Detecting the Severity Level of Diabetic Retinopathy Using Mask RCNN and Transfer Learning](https://mlsanity.com/paper/2011.14733/) (2020)
- [DRDr: Automatic Masking of Exudates and Microaneurysms Caused By Diabetic Retinopathy Using Mask R-CNN and Transfer Learning](https://mlsanity.com/paper/2007.02026/) (2020)
- [ElixirNet: Relation-aware Network Architecture Adaptation for Medical Lesion Detection](https://mlsanity.com/paper/2003.08770/) (2020)
- [Region Growing with Convolutional Neural Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/2009.11717/) (2020)
- [Mini Lesions Detection on Diabetic Retinopathy Images via Large Scale CNN Features](https://mlsanity.com/paper/1911.08588/) (2019)

## Built on by (most cited first, 4 of 4)

- [Embodied AI-Driven Operation of Smart Cities: A Concise Review](https://mlsanity.com/paper/2108.09823/) (2021)
- [Deep Learning in Healthcare: An In-Depth Analysis](https://mlsanity.com/paper/2302.10904/) (2023)
- [Applications of Machine Learning in Healthcare and Internet of Things (IOT): A Comprehensive Review](https://mlsanity.com/paper/2202.02868/) (2022)
- [PRISM-DR: Per-lesion Retinal Inference with Specialist Models for Diabetic Retinopathy](https://mlsanity.com/paper/2607.19864/) (2026)

## Abstract (arXiv)

Medical Imaging is one of the growing fields in the world of computer vision. In this study, we aim to address the Diabetic Retinopathy (DR) problem as one of the open challenges in medical imaging. In this research, we propose a new lesion detection architecture, comprising of two sub-modules, which is an optimal solution to detect and find not only the type of lesions caused by DR, their corresponding bounding boxes, and their masks; but also the severity level of the overall case. Aside from traditional accuracy, we also use two popular evaluation criteria to evaluate the outputs of our models, which are intersection over union (IOU) and mean average precision (mAP). We hypothesize that this new solution enables specialists to detect lesions with high confidence and estimate the severity of the damage with high accuracy.