TY - GEN
T1 - Methodology for the Creation of a Medical Database
T2 - 2nd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2023
AU - Auccahuasi, Wilver
AU - Linares, Oscar
AU - Urbano, Kitty
AU - Rojas, Karin
AU - Aiquipa, Gabriel
AU - Pando-Ezcurra, Tamara
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - Diabetic retinopathy
KW - Fundus imaging
UR - http://www.scopus.com/inward/record.url?scp=85163589686&partnerID=8YFLogxK
U2 - 10.1109/ICAAIC56838.2023.10141187
DO - 10.1109/ICAAIC56838.2023.10141187
M3 - Contribución a la conferencia
AN - SCOPUS:85163589686
T3 - Proceedings of the 2nd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2023
SP - 1685
EP - 1687
BT - Proceedings of the 2nd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2023
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 4 May 2023 through 6 May 2023
ER -
