TY - GEN
T1 - Neovascularization Detection on Optic Disc Region Using Deep Learning
AU - Carrillo-Gomez, Cesar
AU - Nakano, Mariko
AU - Gonzalez-H.Leon, Ana
AU - Romo-Aguas, Juan Carlos
AU - Quiroz-Mercado, Hugo
AU - Lopez-Garcia, Osvaldo
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - Diabetic Retinopathy (DR) is one of the biggest eye diseases affecting the worldwide population. The DR presents several damages in the retina depending on its grade of advance, although the damages are asymptomatic in almost all cases. The presence of neovascularization (NV) is considered as the worse stage in the DR, and the patients of this stage require urgent treatment to avoid partial or total blindness. Timely detection of the new vessels in the retina can lead to an adequate treatment to avoid vision loss. In this work, we present automatic detection of neovascularization in the optic disc region (NVD) using a deep learning algorithm. We evaluate several deep neural networks (DNNs) to classify between health optic disc region and NVD. The better DNNs are DenseNet-161 and Efficientnet-B7, which show 93.3% and 92.0% accuracy and 89.5% and 84.2% in sensitivity, respectively. In the computational complexity, DenseNet-161 has a lower number of trainable parameters than that of Efficientnet-B7. To train the DNNs appropriately, we construct a labeled dataset from one of the largest public datasets, bounding NV regions in the retinal images.
AB - Diabetic Retinopathy (DR) is one of the biggest eye diseases affecting the worldwide population. The DR presents several damages in the retina depending on its grade of advance, although the damages are asymptomatic in almost all cases. The presence of neovascularization (NV) is considered as the worse stage in the DR, and the patients of this stage require urgent treatment to avoid partial or total blindness. Timely detection of the new vessels in the retina can lead to an adequate treatment to avoid vision loss. In this work, we present automatic detection of neovascularization in the optic disc region (NVD) using a deep learning algorithm. We evaluate several deep neural networks (DNNs) to classify between health optic disc region and NVD. The better DNNs are DenseNet-161 and Efficientnet-B7, which show 93.3% and 92.0% accuracy and 89.5% and 84.2% in sensitivity, respectively. In the computational complexity, DenseNet-161 has a lower number of trainable parameters than that of Efficientnet-B7. To train the DNNs appropriately, we construct a labeled dataset from one of the largest public datasets, bounding NV regions in the retinal images.
KW - Automatic diagnostic
KW - Deep learning
KW - Deep neural networks
KW - Diabetic Retinopathy
KW - Neovascularization
UR - http://www.scopus.com/inward/record.url?scp=85111375867&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-77004-4_11
DO - 10.1007/978-3-030-77004-4_11
M3 - Contribución a la conferencia
AN - SCOPUS:85111375867
SN - 9783030770037
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 111
EP - 120
BT - Pattern Recognition - 13th Mexican Conference, MCPR 2021, Proceedings
A2 - Roman-Rangel, Edgar
A2 - Kuri-Morales, Ángel Fernando
A2 - Martínez-Trinidad, José Francisco
A2 - Carrasco-Ochoa, Jesús Ariel
A2 - Olvera-López, José Arturo
PB - Springer Science and Business Media Deutschland GmbH
T2 - 13th Mexican Conference on Pattern Recognition, MCPR 2021
Y2 - 23 June 2021 through 26 June 2021
ER -