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

Added Title

IEEE Global Conference on Life Sciences and Technologies (2nd : 2020)

College

Gokongwei College of Engineering

Department/Unit

Electronics And Communications Engg

Document Type

Conference Proceeding

Source Title

LifeTech 2020 - 2020 IEEE 2nd Global Conference on Life Sciences and Technologies

First Page

235

Last Page

239

Publication Date

3-1-2020

Abstract

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.

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Digitial Object Identifier (DOI)

10.1109/LifeTech48969.2020.1570619208

Disciplines

Biomedical Engineering and Bioengineering

Keywords

Genetic algorithms; Neural networks (Computer science); Blood cells

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