TY - JOUR
T1 - Classification of Eye Diseases in Fundus Images
AU - Bernabe, Omar
AU - Acevedo, Elena
AU - Acevedo, Antonio
AU - Carreno, Ricardo
AU - Gomez, Sandra
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2021
Y1 - 2021
N2 - Eye diseases have been a severe problem worldwide, especially in developing countries where technology and finance are limited. Today, the problem is being resolved thanks to the task of classification that is part of pattern recognition. Its primary goal is to group standard features from any entity, object, phenomenon, or event belonging to the real or abstract world. Convolutional Neural Networks are a type of Artificial Neural Network used in intelligent pattern classification, Machine Learning, and Data Mining. Also, medicine and ophthalmology used these algorithms for detecting diseases in the human body. This work presents a novel intelligent pattern classification algorithm based on a Convolutional Neural network, which is validated through the K-Fold Cross Validation test. Two different groups of retinography images are given: Glaucoma and Diabetic Retinopathy. The result of accuracy percentage was 99.89%. Numerical metrics: Accuracy, Recall, Specificity Precision, and F1 score with values close to 1, and ROC curves support the suitable performance of the proposed classifier.
AB - Eye diseases have been a severe problem worldwide, especially in developing countries where technology and finance are limited. Today, the problem is being resolved thanks to the task of classification that is part of pattern recognition. Its primary goal is to group standard features from any entity, object, phenomenon, or event belonging to the real or abstract world. Convolutional Neural Networks are a type of Artificial Neural Network used in intelligent pattern classification, Machine Learning, and Data Mining. Also, medicine and ophthalmology used these algorithms for detecting diseases in the human body. This work presents a novel intelligent pattern classification algorithm based on a Convolutional Neural network, which is validated through the K-Fold Cross Validation test. Two different groups of retinography images are given: Glaucoma and Diabetic Retinopathy. The result of accuracy percentage was 99.89%. Numerical metrics: Accuracy, Recall, Specificity Precision, and F1 score with values close to 1, and ROC curves support the suitable performance of the proposed classifier.
KW - Artificial intelligence
KW - convolutional neural networks
KW - machine learning
KW - supervised learning
UR - http://www.scopus.com/inward/record.url?scp=85111560685&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2021.3094649
DO - 10.1109/ACCESS.2021.3094649
M3 - Artículo
AN - SCOPUS:85111560685
SN - 2169-3536
VL - 9
SP - 101267
EP - 101276
JO - IEEE Access
JF - IEEE Access
M1 - 9474508
ER -