Microaneurysm Detection in Fundus Images Using a Two-step Convolutional Neural Network

Overview

Journal Biomed Eng Online

Publisher Biomed Central

Specialty Biomedical Engineering

Date 2019 May 31

PMID 31142335

Citations 25

Authors

Noushin Eftekhari

Hamid-Reza Pourreza

Mojtaba Masoudi

Kamaledin Ghiasi-Shirazi

Ehsan Saeedi

Affiliations

Soon will be listed here.

Abstract

Background And Objectives: Diabetic retinopathy (DR) is the leading cause of blindness worldwide, and therefore its early detection is important in order to reduce disease-related eye injuries. DR is diagnosed by inspecting fundus images. Since microaneurysms (MA) are one of the main symptoms of the disease, distinguishing this complication within the fundus images facilitates early DR detection. In this paper, an automatic analysis of retinal images using convolutional neural network (CNN) is presented.

Methods: Our method incorporates a novel technique utilizing a two-stage process with two online datasets which results in accurate detection while solving the imbalance data problem and decreasing training time in comparison with previous studies. We have implemented our proposed CNNs using the Keras library.

Results: In order to evaluate our proposed method, an experiment was conducted on two standard publicly available datasets, i.e., Retinopathy Online Challenge dataset and E-Ophtha-MA dataset. Our results demonstrated a promising sensitivity value of about 0.8 for an average of >6 false positives per image, which is competitive with state of the art approaches.

Conclusion: Our method indicates significant improvement in MA-detection using retinal fundus images for monitoring diabetic retinopathy.

Citing Articles

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A deep learning-based ADRPPA algorithm for the prediction of diabetic retinopathy progression.

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Automatic Detection of Microaneurysms in OCT Images Using Bag of Features.

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Localization and grading of NPDR lesions using ResNet-18-YOLOv8 model and informative features selection for DR classification based on transfer learning.

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Resilient back-propagation machine learning-based classification on fundus images for retinal microaneurysm detection.

Steffi S, Sam Emmanuel W Int Ophthalmol. 2024; 44(1):91.

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