Ryan Carreon Reyes
Research Subjects
Ryan Carreon Reyes has a total of 10 co-authors. Here are the top co-authors by number of shared publications:
- Alvin Sarraga Alon
Technological Institute of the Philippines,Department of Computer Engineering,Manila,Philippines
2 shared publications Ace C. Lagman
FEU Institute of Technology,Information Technology Dept.,Manila,Philippines
1 shared publication- Alexander A. Hernandez
College of Computer Studies and Multimedia Arts, FEU Institute of Technology, Manila 1015, Philippines
1 shared publication Gemma D. Belga
Technological University of the Philippines,College of Industrial Technology,Department of Electronics,Manila,Philippines
1 shared publication- Jonel R. Macalisang
Technology Licensing Office- ITSO, Technological University of the Philippines, Manila, Philippines
1 shared publication Julie Ann B. Susa
Southern Luzon State University,College of Engineering,Lucban,Quezon,Philippines
1 shared publication
Latest Publications
- 26 October 2022
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 0488-0495
A Multistage Transfer Learning Approach for Acute Lymphoblastic Leukemia Classification
Automated medical image analysis driven by artificial intelligence can revolutionize modern healthcare in producing swift and precise diagnostics. Due to doctors’ varying breadths of training and expertise, traditional leukemia screening methods frequently involve considerable subjectivity. Using a 3-stage transfer learning approach and stacks of convolutional neural networks, we constructed an efficient pathway for automatic leukemia identification and classification through various phases. Experimental findings disclosed that our pipeline powered by InceptionResNetV2 architecture decisively affects the accuracy with 99.60% (normal vs. leukemia) and 94.67% (normal to L3). Moreover, it reduces error rates by 1.65% and 6.05%, respectively. A consistent result via the T-test confirms our proposed framework robustness with a significant positive difference of 4.71% over the standard transfer learning mechanism (p-value = 0.0001 & t = 0.85310). This research could aid and support oncologists in early yet reliable prognoses of acute lymphoblastic leukemia types.
- 26 October 2022
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 0488-0495
A Multistage Transfer Learning Approach for Acute Lymphoblastic Leukemia Classification
Automated medical image analysis driven by artificial intelligence can revolutionize modern healthcare in producing swift and precise diagnostics. Due to doctors’ varying breadths of training and expertise, traditional leukemia screening methods frequently involve considerable subjectivity. Using a 3-stage transfer learning approach and stacks of convolutional neural networks, we constructed an efficient pathway for automatic leukemia identification and classification through various phases. Experimental findings disclosed that our pipeline powered by InceptionResNetV2 architecture decisively affects the accuracy with 99.60% (normal vs. leukemia) and 94.67% (normal to L3). Moreover, it reduces error rates by 1.65% and 6.05%, respectively. A consistent result via the T-test confirms our proposed framework robustness with a significant positive difference of 4.71% over the standard transfer learning mechanism (p-value = 0.0001 & t = 0.85310). This research could aid and support oncologists in early yet reliable prognoses of acute lymphoblastic leukemia types.
- 18 December 2020
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 1-5
Watercraft-Net: A Deep Inference Vision Approach of Watercraft Detection for Maritime Surveillance System Using Optical Aerial Images
The ocean and any form of bodies of water must be protected and secure from any intruders and to monitor our ocean, technology must be used and integrated for more efficient monitoring. Automatic boat detection plays an important role in maritime surveillance. However, the maritime environment represents lots of challenges such as the wave of water, boat movements, and weather condition. This paper presents a method for detecting moving boats from a sequence of images using a deep learning approach. In this study, the researchers proposed a detection system for the boats in the ocean using optical aerial images. The researchers conducted testing and the results were favorable. Upon testing the researchers obtained a 90% accuracy of detection of the ship in the ocean using the single images, video feeds, and live feeds. The experiments show promising results.
- 18 December 2020
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 1-5
Watercraft-Net: A Deep Inference Vision Approach of Watercraft Detection for Maritime Surveillance System Using Optical Aerial Images
The ocean and any form of bodies of water must be protected and secure from any intruders and to monitor our ocean, technology must be used and integrated for more efficient monitoring. Automatic boat detection plays an important role in maritime surveillance. However, the maritime environment represents lots of challenges such as the wave of water, boat movements, and weather condition. This paper presents a method for detecting moving boats from a sequence of images using a deep learning approach. In this study, the researchers proposed a detection system for the boats in the ocean using optical aerial images. The researchers conducted testing and the results were favorable. Upon testing the researchers obtained a 90% accuracy of detection of the ship in the ocean using the single images, video feeds, and live feeds. The experiments show promising results.