An automated retinal hemorrhages and exudates detection in a Mexican image set

J. A. Echeveste-Vázquez; J. C. Armas-Pérez; A. González-Vega; G. Soto-Cruz; C. Villaseñor-Mora

doi:10.1117/12.2677845

4 October 2023 An automated retinal hemorrhages and exudates detection in a Mexican image set

J. A. Echeveste-Vázquez, J. C. Armas-Pérez, A. González-Vega, G. Soto-Cruz, C. Villaseñor-Mora

Proceedings Volume 12675, Applications of Machine Learning 2023; 126750L (2023) https://doi.org/10.1117/12.2677845
Event: SPIE Optical Engineering + Applications, 2023, San Diego, California, United States

Abstract

Retinopathy is a common complication of diabetes that can cause severe vision loss if not detected and managed promptly. In this study, we propose a comprehensive approach that leverages image processing techniques to analyze fundus images of patients with diabetic retinopathy. Our primary focus is on vein extraction and hemorrhage detection, with exudate detection being performed only on specific images to showcase advancements in the current prototype algorithm. The dataset used in this project consists of images obtained from Mexican ophthalmology institutes, ensuring its relevance and applicability to the local population. By extracting veins and hemorrhages, we aim to capture crucial features indicative of the severity of retinopathy. These generated images, along with the original dataset, are utilized to train convolutional neural network (CNN) models, enabling accurate classification of the disease's degree into three categories. The significance of this project lies in its potential to serve as an auxiliary tool in diagnosing diabetic retinopathy. By automating the analysis of fundus images and providing objective classification results, our algorithm aims to assist healthcare professionals in making informed decisions regarding treatment and management options. The proposed method can potentially enhance the efficiency and precision of diabetic retinopathy (DR) diagnosis, improving Mexican health outcomes.

Conference Presentation

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

J. A. Echeveste-Vázquez, J. C. Armas-Pérez, A. González-Vega, G. Soto-Cruz, and C. Villaseñor-Mora "An automated retinal hemorrhages and exudates detection in a Mexican image set", Proc. SPIE 12675, Applications of Machine Learning 2023, 126750L (4 October 2023); https://doi.org/10.1117/12.2677845

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