![](/image/LogoVKU.png)
Please use this identifier to cite or link to this item:
https://elib.vku.udn.vn/handle/123456789/2306
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Nguyen, Thi Tham | - |
dc.contributor.author | Vo, Kieu Hoa | - |
dc.contributor.author | Nguyen, Xuan Vinh | - |
dc.contributor.author | Do, Trong Hop | - |
dc.date.accessioned | 2022-08-16T07:23:40Z | - |
dc.date.available | 2022-08-16T07:23:40Z | - |
dc.date.issued | 2022-07 | - |
dc.identifier.issn | 978-604-84-6711-1 | - |
dc.identifier.uri | http://elib.vku.udn.vn/handle/123456789/2306 | - |
dc.description | The 11th Conference on Information Technology and its Applications; Topic: Image and Natural Language Processing; pp.192-200. | vi_VN |
dc.description.abstract | In this paper, we perform the problem of X-ray image classification between Covid infected lungs and normal lungs by using Apache Spark. Our system deploys deep learning models on Spark framework by using BigDL library in order to handle a big data of chest X-ray images with high performance. The deep learning models used are CNN, Resnet50, VGG90, InceptionV3 trained on a big data of more than 13.000 chest X-ray images for COVID-19 positive cases along with Normal cases. We achieved pretty good results with all deep learning models, in which the InceptionV3 model gave the highest performance of 93.75% with accuracy measurement. | vi_VN |
dc.language.iso | en | vi_VN |
dc.publisher | Da Nang Publishing House | vi_VN |
dc.subject | Big data | vi_VN |
dc.subject | Apache Spark | vi_VN |
dc.subject | Classification | vi_VN |
dc.subject | Deep Learning | vi_VN |
dc.subject | BigDL | vi_VN |
dc.title | An Upscalable Distributed Deep Learning based System for Multi-class Xray Covid19 Classification | vi_VN |
dc.type | Working Paper | vi_VN |
Appears in Collections: | CITA 2022 |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.