Peer Review_Identification of White Blood Cells Using Machine Learning Classification Based on Feature Extraction

Yudhana, Anton (2021) Peer Review_Identification of White Blood Cells Using Machine Learning Classification Based on Feature Extraction. UIN Sunan Gunung Djati Bandung.

Text 14_Identification of White Blood Cells Using Machine Learning Classification Based on Feature Extraction.pdf Download (1MB)

Abstract

In various disease diagnoses, one of the parameters is white blood cells, consisting of eosinophils, basophils, neutrophils, lymphocytes, and monocytes. Manual identification takes a long time and tends to be subjective depending on the staff's experience,so the automatic identification of white blood cells will be faster and more accurate. White blood cells are identified by examininga colored blood smear (SADT) and examined under a digital microscope to obtain a cell image. Image identification of whiteblood cells is determined through HSV color space segmentation (Hue, SaturationValue) and feature extraction of the Gray Level Cooccurrence Matrix (GLCM) method using the Angular Second Moment (ASM), Contrast, Entropy, and Inverse Different Moment (IDM) features. The purpose of this study was to identify white blood cells by comparing the classification accuracy of the K-nearest neighbor (KNN), Naïve Bayes Classification (NBC), and Multilayer Perceptron (MLP) methods. The classification results of 100 training data and 50 white blood cell image testing data. Tests on the KNN, NBC, and MLP methods yielded an accuracy of 82%, 80%, and 94%, respectively. Therefore, MLP was chosen as the best classification model in the identification of white blood cells

Item Type:	Other
Subjects:	T Technology > T Technology (General)
Divisi / Prodi:	Faculty of Industrial Technology (Fakultas Teknologi Industri) > S1-Electrical Engineering (S1-Teknik Elektro)
Depositing User:	Anton Yudhana,
Date Deposited:	15 Jul 2022 02:14
Last Modified:	18 Jul 2022 04:50
URI:	http://eprints.uad.ac.id/id/eprint/35862

Actions (login required)

View Item