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  • ElecEng
  • GC-ElecEng 2021
  • 63.Cnf-1184
ElecEng Congresses with Published Papers
GC-ElecEng 2020, Valencia
GC-ElecEng 2021, Valencia
Papers Published at GC-ElecEng 2021
All 11 Papers
IDAuthors and TitlePages
43.Cnf-38 Mr. Aron Kondoro
Ms. Diana Rwegasira
Prof. Imed Dhaou
Prof. Hannu Tenhunen
Trends of using blockchain technology in the smart grid
63.Cnf-75 Mr. Andres Rojas
Prof. Gordana J. Dolecek
Evaluation of Supervised Machine Learning Classification Algorithms for Fingerprint Recognition
62.Cnf-106 Mr. Joan Carles Montero
Ms. Merce Feliu
Dr. Joan Bas
Optical Communications Through Low Orbit Satellites
63.Cnf-140 Dr. Mohammad Al-Mousa
Mr. Nael Sweerky
Dr. Ghassan Samara
Dr. Mohammed Alghanim
Ms. Abla Hussein
Mr. Braa Qadoumi
General Countermeasures of Anti-Forensics Categories
62.Cnf-143 Dr. Ghassan Samara
Mr. Mohammad Hussein
Dr. Khalid Alqawasmi
Alarm System at street junctions (ASSJ) to avoid accidents Using VANET system
43.Cnf-1170 Mr. Zayd Khashshan
Dr. Moudar Zgoul
Optic Flow-based Vision System for Autonomous and Collision-free Navigation of Micro Aerial Vehicles
62.Cnf-1173 Mr. Fernando Salazar-Quiñonez
Prof. Raed Abd-Alhameed
Mr. Andrew Cowley
Mr. Derek Bladen
UHF Radio Extender System for Ship to Shore Communications using 3D SBR for Positioning
63.Cnf-1174 Prof. Gordana J. Dolecek
Improving Characteristics of Compensated Sharpened CIC Decimation Filters
35.Cnf-1175 Ms. Aika Silveira Miura
Ms. Mar Parra Boronat
Prof. Jaime Lloret
Prof. Miguel Rodilla
LED optical sensor prototype to determine dissolved oxygen saturation in water
35.Cnf-1176 Dr. Falih Alkhafaji
Modelling High speed Robotic Arm for Industrial Applications
43.Cnf-1177 Mr. Juan Luis Mayor Puyol
Dr. Victor Monzón Baeza
Bycicle Sharing System Using an IoT Network
63.Cnf-1184 Paper View Page
Title Detection of Sickle Cell, Megaloblastic Anemia, Thalassemia and Malaria through Convolutional Neural Network
Authors Dr. Enas Abdulhay, Jordan University of Science and Technology, Irbid, Jordan
Mr. Ahmad Allow, Jordan University of Science and Technology, Irbid, Jordan
Mr. Mohammad Al-Jalouly, Jordan University of Science and Technology, Irbid, Jordan
Abstract This paper presents an alternative method to diagnose Malaria and Anemia (Sickle Cell Anemia, Megaloblastic Anemia and Thalassemia) as well as to differentiate between them. First, different related high resolution images of blood samples are taken from multiple datasets. Second, Convolutional Neural Networks technique is implemented and applied in order to process the images without the need of the standard protocol of Complete Blood Count (CBC) test. The implemented convolutional Neural Network (CNN) has been designed using Python to train on a number of microscopic images. After completing the training phase, the built model has been tested on other images to classify them into normal blood cells, Malaria, Sickle cell anemia, Megaloblastic anemia or Thalassemia. Third, the diagnosis is made based on the outcomes. Finally, the accuracy of results is assessed. The total accuracy of the test is 93.4%. The suggested approach yields promising outcomes that help diagnose blood samples faster, with low cost as well as without the need of an analysis laboratory.
Track Image: Digital Image Processing
Conference 2nd Mosharaka International Conference on Digital Signal Processing (MIC-Signals 2021)
Congress 2021 Global Congress on Electrical Engineering (GC-ElecEng 2021), 10-12 December 2021, Valencia, Spain
Pages --1
Topics Pattern Recognition
Image Classification and Clustering
ISSN 2227-331X
BibTeX @inproceedings{1184ElecEng2021,
title={Detection of Sickle Cell, Megaloblastic Anemia, Thalassemia and Malaria through Convolutional Neural Network },
author={Enas Abdulhay, and Ahmad Allow, and Mohammad Al-Jalouly},
booktitle={2021 Global Congress on Electrical Engineering (GC-ElecEng 2021)},
organization={Mosharaka for Research and Studies} }
Paper Views 4 Paper Views Rank 104/524
Paper Downloads 0 Paper Downloads Rank 409/524
GC-ElecEng 2021 Visits: 1569||MIC-Signals 2021 Visits: 333||Image Track Visits: 52