# Automated Detection of Microaneurysms in Color Fundus Images using Deep Learning with Different Preprocessing Approaches

arXiv: https://arxiv.org/abs/2004.09493 · HTML: https://mlsanity.com/paper/2004.09493/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Apr 2020.

## Built on by (most cited first, 5 of 5)

- [RTNet: Relation Transformer Network for Diabetic Retinopathy Multi-lesion Segmentation](https://mlsanity.com/paper/2201.11037/) (2022)
- [AOSLO-net: A deep learning-based method for automatic segmentation of retinal microaneurysms from adaptive optics scanning laser ophthalmoscope images](https://mlsanity.com/paper/2106.02800/) (2021)
- [Medical image registration using unsupervised deep neural network: A scoping literature review](https://mlsanity.com/paper/2208.01825/) (2022)
- [Automatic Detection of Microaneurysms in OCT Images Using Bag of Features](https://mlsanity.com/paper/2205.04695/) (2022)
- [A comprehensive survey on computer-aided diagnostic systems in diabetic retinopathy screening](https://mlsanity.com/paper/2208.01810/) (2022)

## Abstract (arXiv)

Imaging methods by using computer techniques provide doctors assistance at any time and relieve their workload, especially for iterative processes like identifying objects of interest such as lesions and anatomical structures from the image. Detection of microaneurysms (MAs) as one of the lesions in the retina is considered to be a crucial step in some retinal image analysis algorithms for the identification of diabetic retinopathy (DR) as the second-largest eye diseases in developed countries. The objective of this study is to compare the effect of two preprocessing methods, Illumination Equalization, and Top-hat transformation, on retinal images to detect MAs using a combination of Matching based approach and deep learning methods either in the normal fundus images or in the presence of DR. The steps for the detection are as following: 1) applying preprocessing, 2) vessel segmentation and masking, and 3) MAs detection using a combination of Matching based approach and deep learning. From the accuracy viewpoint, we compared the method to manual detection performed by ophthalmologists for our big retinal image databases (more than 2200 images). Using first preprocessing method, Illumination equalization and contrast enhancement, the accuracy of MAs detection was about 90% for all databases (one local and two publicly retinal databases). The performance of the MAs detection methods using top-hat preprocessing (the second preprocessing method) was more than 80% for all databases.