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Update on Wednesday, 24 January 2024: Prof. Arwa Zabian becomes Chairperson of MIC-Wireless 2024.
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  • EngiTek 2020
  • 47.Cnf-1130
Engineering Congresses with Published Papers
Papers Published at EngiTek 2020
All 8 Papers
IDAuthors and TitlePages
51.Cnf-17 Mr. Ameer Mubaslat
Mr. Ahmad Al Haj
Prof. Saud Khashan
Simulation assisted leak detection in pressurized systems using machine learning
1-6
51.Cnf-1062 Dr. Aqeed M. Chyad
Dr. Osama Abudayyeh
Dr. Maha Alkasisbeh
A Nonlinear Regression-Based Machine Learning Model for Predicting Concrete Bridge Deck Condition
7-11
47.Cnf-1071 Dr. Mohammad M. Banat
Mr. Ahmed Al-Shwmi
Detailed Simplified Implementation of Filter Bank Multicarrier Modulation Using Sub-Channel Prototype Filters
12-18
47.Cnf-1097 Dr. Mohammad M. Banat
Mrs. Haifaa Elayyan
Academic Rewards Coin (ARC) Concept: Mining Academic Contribution Into Bitcoin
19-27
47.Cnf-1098 Mr. Chris Matrakidis
Dr. Dimitris Uzunidis
Alexandros Stavdas
Prof. Gerasimos Pagiatakis
Analytical modeling of dispersion penalty for NRZ transmission
28-32
47.Cnf-1099 Dr. Dimitris Uzunidis
Mr. Chris Matrakidis
Alexandros Stavdas
Prof. Gerasimos Pagiatakis
On the Attainable Transparent Length of Multi-Band Optical Systems Employing Rare – Earth Doped Fiber Amplifiers
33-38
47.Cnf-1101 Mr. Georgios D. Kolezas
Dr. Grigorios Zouros
Prof. Gerasimos Pagiatakis
Complex Magnetic and Electric Dipolar Resonances of Subwavelength GaAs Prolate Spheroids
39-42
51.Cnf-1104 Dr. Rafat Al-Waked
Underground Car Park - Mechanical Ventilation
43-48
47.Cnf-1130 Paper View Page
Title Automatic Detection of Acute Lymphoblastic Leukemia Using Machine Learning
Authors Ms. Lamis Bany Issa, Jordan University of Science and Technology, Irbid, Jordan
Dr. Areen Al-Bashir, Jordan University of Science and Technology, Irbid, Jordan
Abstract The identification of acute leukaemia blast cells in coloured microscopic images is a challenging task. Usually it is performed by visual assessment for microscopic images of blood samples. However, considering the quick advances in utilizing different image processing techniques; rapid and more accurate assessment can be achieved. Therefore, this paper proposed an enhanced automatic method to detect Acute Lymphoblastic Leukemia (ALL) utilizing microscopic blood sample images. Our proposed methodology includes; Colour-based segmentation using K-Means clustering technique together with morphological operations and feature extraction algorithms and finally cell classification. The proposed method was tested on blood microscopic images from ALL-IDB1 database, University of Milano, Italy. ALL_IDB1 comprises of 108 blood cells images (healthy and leukaemia) in which the lymphocytes are lapelled by expert oncologists. The accuracy achieved was 96.3%. this relatively high accuracy suggests that the Colour-based method for segmentation was more efficient and acceptable compared to the traditional thresholding methods and the methods that not considered the overlapped cells.
Track BME: Biomedical Engineering
Conference 1st International Conference on Electrical Engineering and Technologies (ElectriTek 2020)
Congress International Congress on Engineering Technologies (EngiTek 2020), 16-18 June 2020, Irbid, Jordan
Pages --1
Topics Biomedical Signal Processing
Biomedical Imaging Systems
ISSN 2227-331X
DOI
BibTeX @inproceedings{1130EngiTek2020,
title={Automatic Detection of Acute Lymphoblastic Leukemia Using Machine Learning},
author={Lamis Bany Issa, and Areen Al-Bashir},
booktitle={International Congress on Engineering Technologies (EngiTek 2020)},
year={2020},
pages={--1},
doi={}},
organization={Mosharaka for Research and Studies} }
Paper Views 50 Paper Views Rank 157/524
Paper Downloads 14 Paper Downloads Rank 373/524
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