Carlos Silva

Orcid: 0000-0001-8362-8984

Affiliations:
  • Federal Institute of Education, Science and Technology Farroupilha, Alegrete,Brazil
  • Federal University of Pelotas, Postgraduate Program in Computing (PPGC), Brazil
  • Federal University of Pampa, Alegrete, Brazil (2015 - 2017)


According to our database1, Carlos Silva authored at least 10 papers between 2017 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

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Online presence:

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Bibliography

2024
DRAT: A semi-supervised tool for automatic annotation of lesions caused by diabetic retinopathy.
Proceedings of the 20th Brazilian Symposium on Information Systems, 2024

2023
A New Approach for Fundus Lesions Instance Segmentation Based on Mask R-CNN X101-FPN Pre-Trained Architecture.
IEEE Access, 2023

Detection of retinal microlesions through YOLOR-CSP architecture and image slicing with the SAHI algorithm.
Proceedings of the International Joint Conference on Neural Networks, 2023

2022
A New Approach for Detecting Fundus Lesions Using Image Processing and Deep Neural Network Architecture Based on YOLO Model.
Sensors, 2022

A Method Based on Deep Neural Network for Instance Segmentation of Retinal Lesions Caused by Diabetic Retinopathy.
Proceedings of the International Conference on Computational Science and Computational Intelligence, 2022

2021
Deep Neural Network Model based on One-Stage Detector for Identifying Fundus Lesions.
Proceedings of the International Joint Conference on Neural Networks, 2021

Detection of Fundus Lesions through a Convolutional Neural Network in Patients with Diabetic Retinopathy.
Proceedings of the 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society, 2021

A New Method Based on Deep Learning to Detect Lesions in Retinal Images using YOLOv5.
Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, 2021

2018
The Pattern Recognition in Cattle Brand using Bag of Visual Words and Support Vector Machines Multi-Class.
Inteligencia Artif., 2018

2017
Deep Learning Techniques Applied to the Cattle Brand Recognition.
Proceedings of the Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, 2017


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