# DRDr II: Detecting the Severity Level of Diabetic Retinopathy Using Mask RCNN and Transfer Learning

arXiv: https://arxiv.org/abs/2011.14733 · HTML: https://mlsanity.com/paper/2011.14733/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Nov 2020.

## Built on by (most cited first, 6 of 6)

- [Embodied AI-Driven Operation of Smart Cities: A Concise Review](https://mlsanity.com/paper/2108.09823/) (2021)
- [DRDrV3: Complete Lesion Detection in Fundus Images Using Mask R-CNN, Transfer Learning, and LSTM](https://mlsanity.com/paper/2108.08095/) (2021)
- [The Use of Video Captioning for Fostering Physical Activity](https://mlsanity.com/paper/2104.03207/) (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)
- [Decoding the Alphabet Soup of Degrees in the United States Postsecondary Education System Through Hybrid Method: Database and Text Mining](https://mlsanity.com/paper/2309.13050/) (2023)

## Abstract (arXiv)

DRDr II is a hybrid of machine learning and deep learning worlds. It builds on the successes of its antecedent, namely, DRDr, that was trained to detect, locate, and create segmentation masks for two types of lesions (exudates and microaneurysms) that can be found in the eyes of the Diabetic Retinopathy (DR) patients; and uses the entire model as a solid feature extractor in the core of its pipeline to detect the severity level of the DR cases. We employ a big dataset with over 35 thousand fundus images collected from around the globe and after 2 phases of preprocessing alongside feature extraction, we succeed in predicting the correct severity levels with over 92% accuracy.