# Novel Fundus Image Preprocessing for Retcam Images to Improve Deep Learning Classification of Retinopathy of Prematurity

arXiv: https://arxiv.org/abs/2302.02524 · HTML: https://mlsanity.com/paper/2302.02524/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 48 references that are in the map.
- First posted on arXiv 6 Feb 2023.

## Builds on (most cited first, 8 of 8)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Image processing](https://mlsanity.com/paper/1409.2413/) (2014)
- [The Little W-Net That Could: State-of-the-Art Retinal Vessel Segmentation with Minimalistic Models](https://mlsanity.com/paper/2009.01907/) (2020)
- [Artificial Intelligence for Pediatric Ophthalmology](https://mlsanity.com/paper/1904.08796/) (2019)
- [Retinopathy of Prematurity Stage Diagnosis Using Object Segmentation and Convolutional Neural Networks](https://mlsanity.com/paper/2004.01582/) (2020)
- [Enhancement of Retinal Fundus Images via Pixel Color Amplification](https://mlsanity.com/paper/2007.14456/) (2020)
- [Early Detection of Retinopathy of Prematurity stage using Deep Learning approach](https://mlsanity.com/paper/2109.01442/) (2021)
- [A Novel Image Dehazing and Assessment Method](https://mlsanity.com/paper/2001.06963/) (2020)

## Built on by (most cited first, 1 of 1)

- [Current and future roles of artificial intelligence in retinopathy of prematurity](https://mlsanity.com/paper/2402.09975/) (2024)

## Abstract (arXiv)

Retinopathy of Prematurity (ROP) is a potentially blinding eye disorder because of damage to the eye's retina which can affect babies born prematurely. Screening of ROP is essential for early detection and treatment. This is a laborious and manual process which requires trained physician performing dilated ophthalmological examination which can be subjective resulting in lower diagnosis success for clinically significant disease. Automated diagnostic methods can assist ophthalmologists increase diagnosis accuracy using deep learning. Several research groups have highlighted various approaches. Captured ROP Retcam images suffer from poor quality. This paper proposes the use of improved novel fundus preprocessing methods using pretrained transfer learning frameworks to create hybrid models to give higher diagnosis accuracy. Once trained and validated, the evaluations showed that these novel methods in comparison to traditional imaging processing contribute to better and in many aspects higher accuracy in classifying Plus disease, Stages of ROP and Zones in comparison to peer papers.