Implementation of CNN based COVID-19 classification model from CT images

被引：4

作者：

Kaya, Atakan ^{[1
]}

Atas, Kubilay ^{[1
]}

Myderrizi, Indrit ^{[1
]}

机构：

[1] Istanbul Gelisim Univ, Elect Elect Engn, Istanbul, Turkey

来源：

2021 IEEE 19TH WORLD SYMPOSIUM ON APPLIED MACHINE INTELLIGENCE AND INFORMATICS (SAMI 2021) | 2021年

关键词：

Covid-19; Deep Learning; Classification; Computed Tomography;

D O I：

10.1109/SAMI50585.2021.9378646

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The number of COVID-19 patients around the globe is increasing day by day. Statistics show that even after almost 10 months from outbreak, number of the total patients has not reached to its peak value yet. Easy spreading of the virus among people causes high number of patients at the same time. Accelerating the reduction in spread is of vital importance. In order to achieve this reduction, early diagnosis of the disease and the number of tests and scans to be performed frequently becomes important. In this paper, a comprehensive model examination is made to overcome COVID-19 diagnosing problem. Using CT images, data augmentation technique is applied first in the pre-processing section and then pre-trained deep CNN networks perform the classification. The model is tested using various networks and high accuracy results of 96.5% and 97.9% are obtained for VGG-16 and EfficientNetB3 networks, respectively.

引用

页码：201 / 206

页数：6

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