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Methodology for the Creation of a Medical Database: Case of Fundus imaging | IEEE Conference Publication | IEEE Xplore

Methodology for the Creation of a Medical Database: Case of Fundus imaging


Abstract:

Currently, recognition systems based on the use of Artificial Intelligence techniques, are being used more frequently. For these systems to be trained, it is necessary to...Show More

Abstract:

Currently, recognition systems based on the use of Artificial Intelligence techniques, are being used more frequently. For these systems to be trained, it is necessary to have a series of training images, known as dataset of images. This research study demonstrates a novel method to create a dataset of different types of images, with a demonstration applied to fundus images, in order to recognize exudates that are the first symptoms of diabetic retinopathy. The proposed method considers original fundus images, which identify characteristics of some pathology, and in this case, diabetic retinopathy. With these images, groups of images are generated to be able to build a dataset of images and that these can be used in the design of classification algorithms. As a result, this study presents a new dataset corresponding to image areas with presence of hard exudates. The proposed method can be scaled to different types and modalities of images.
Date of Conference: 04-06 May 2023
Date Added to IEEE Xplore: 08 June 2023
ISBN Information:
Conference Location: Salem, India

I. Introduction

The fundus images are those dedicated to being able to analyze the retina by means of a non-invasive method, for the present work the use of the fundus images from the DIARETDB1 database is used, which has one of the characteristics main, the identification of the pathologies present in the images, as is the case of hard and soft exudates and hemorrhages mainly, from these images we generate the database to implement [1]. Applying these same concepts to medical imaging, we find works that resort to the use and storage of fundus images for the recognition of patterns related to diabetic retinopathy [2].

References

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