Diagnosis of Coronavirus Disease From Chest X-Ray Images Using DenseNet-169 Architecture

被引：0

作者：

Dalvi P.P. ^{[1
]}

Edla D.R. ^{[1
]}

Purushothama B.R. ^{[1
]}

机构：

[1] Department of Computer Science and Engineering, National Institute of Technology, Goa

来源：

SN Computer Science | / 4卷 / 3期

关键词：

AlexNet; Chest X-rays; COVID-19; DenseNet-169; ResNet-50; VGG-16; VGG-19;

D O I：

10.1007/s42979-022-01627-7

中图分类号：

学科分类号：

摘要：

The coronavirus disease (COVID-19) is a very contagious and dangerous disease that affects the human respiratory system. Early detection of this disease is very crucial to contain the further spread of the virus. In this paper, we proposed a methodology using DenseNet-169 architecture for diagnosing the disease from chest X-ray images of the patients. We used a pretrained neural network and then utilised the transfer learning method for training on our dataset. We also used Nearest-Neighbour interpolation technique for data preprocessing and Adam Optimizer at the end for optimization. Our methodology achieved 96.37 % accuracy which was better than that obtained using other deep learning models like AlexNet, ResNet-50, VGG-16, and VGG-19. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

引用

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