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.
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
DOI
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)},
year={2021},
pages={--1},
doi={}},
organization={Mosharaka for Research and Studies}
}
