Detection of Covid-19 in CXR: A Low Sample Size Deep Convolutional Neural Network Training Data Approach

被引：0
|
作者：
Mulgada, Jehoshua [1 ]
Melo, Princess Marie B. [2 ]
Ligayo, Michael Angelo D. [3 ]
Reyes, Ryan Carreon [4 ]
Melegrito, Mark P. [5 ]
机构：
[1] Batangas State Univ, Comp Engn Program, Batangas City, Philippines
[2] Batangas State Univ, Coll Informat & Comp Sci, Batangas City, Philippines
[3] Quezon City Univ, Dept Elect Engn, Quezon City, Philippines
[4] Technol Univ Philippines, Dept Elect Engn, Manila, Philippines
[5] Technol Univ Philippines, Dept Elect & Commun Engn, Manila, Philippines
来源：
2022 INTERNATIONAL CONFERENCE ON DECISION AID SCIENCES AND APPLICATIONS (DASA) | 2022年
关键词：
image detection; deep learning; covid-19; detection; chest x-ray detection; yolov3;
D O I：
10.1109/DASA54658.2022.9765039
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Infectious illness Covid-19 is highly contagious and has claimed the lives of numerous individuals. To assist prevent the virus's transmission, it's critical to identify and isolate those who have been infected with the infection. The purpose of the study is to aid in the detection of Covid-19 alongside with RT-PCR test by utilizing a deep learning algorithm, specifically YOLOv3 as the technique to be used for it uses CNN, which then implements deep learning technique. The study has a promising detection to detect if the person's CXR has Covid-19, normal or viral pneumonia, obtaining an mAP value of 95.27% from model 14, which is the highest among the 12 models created.
引用
收藏
页码：300 / 304
页数：5
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