Faster R-CNN model with momentum optimizer for RBC and WBC variants classification

• Main Authors: Tobias, Rogelio Ruzcko, De Jesus, Luigi Carlo, Mital, Matt Ervin, Lauguico, Sandy C., Guillermo, Marielet, Vicerra, Ryan Rhay P., Bandala, Argel A., Dadios, Elmer
• Format: text
• Published: Animo Repository 2020
• Subjects: Genetic algorithms; Neural networks (Computer science); Blood cells; Biomedical Engineering and Bioengineering
• Online Access: https://animorepository.dlsu.edu.ph/faculty_research/3118
• Institution: De La Salle University

Since many diseases and infections are dependent on the count and type of Red Blood Cells (RBCs) and White Blood Cells (WBCs) present in the blood stream, detection and classification pertaining to them is necessary and relevant. Based from existing related literature, ordinary Neural Networks are usually employed. Also, in existing researches, RBC types are the main focus. Hence, after observing research gaps, a Faster Region-based Convolutional Neural Network (Faster R-CNN) was utilized for this study, focusing not only on RBCs but also on the variants of WBCs. The aim is to have a fast and reliable system in order to achieve the goal of aiding the medical field in the classification of RBCs and WBCs. © 2020 IEEE.