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Home > Archives > Volume 20, No 8 (2022) > Article

DOI: 10.14704/nq.2022.20.8.NQ44594

Employing X-ray images Characteristics For Early Corona Virus Detection Based on Deep Learning Techniques

Mohammed L. Muammer, Omar Ibrahim Alsaif

Abstract

SARS-CoV2 is a new virus that began rapidly spreading in Wuhan Province, China, on December 2019 and has since spread around all the world. The infections methods can be considered as transportation techniques were one of the key factors that contributed to the global pandemic that occurred on March, 2020. The virus has the ability to infect various organs of the human body, including lungs, heart, kidneys, and others, causing severe damage and maybe death. As the virus spreads quickly, numerous ways for detecting the infection have emerged to assist doctors in treating the patient. This paper concentrate about using X- ray images that are collected from Kaggle data set to detect the infections of corona virus. These data are pre-trained (fine-tuned) to specific networks like Visual Geometry Group (vgg19) and MobileNet to get benefit of the CNN properties (image processing, segmentation and classification). The motivation of this paper include section (1) introduction with related work, section (2) (CNN)algorithm with details, section (3) contain the the data set properties while section (4) included the results and discussions. The proposed technique (vgg19 network) performed the best result (99% ) validation accuracy.

Keywords

SARS-CoV2 disease, deep learning, CNN, Visual Geometry Group (VGG19), X-ray images.

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