tetano
Editor, Senior Moderator
Expert Syst Appl
. 2022 Feb 24;116740.
doi: 10.1016/j.eswa.2022.116740. Online ahead of print.
A novel algorithm for detection of COVID-19 by analysis of chest CT images using Hopfield neural network
Saeed Sani[SUP] 1 [/SUP], Hossein Ebrahimzadeh Shermeh[SUP] 2 [/SUP]
Affiliations
Abstract
Background: Widely spread of COVID-19 virus has put the whole world in jeopardy. At this moment, using new techniques to detect and treat this novel disease is of significance or may be the first priority of many scientists and researchers through the world.
Purpose: To present a new algorithm for detection of the novel coronavirus 2019 using chest CT images with high accuracy.
Materials and methods: In this study, we looked at the newly-presented data and detection methods of this disease using chest CT; then, a new neural network algorithm was presented to recognize the COVID-19 symptoms. A mathematical model is used to enhance the accuracy of masking, and a high accuracy Hopfield Neural Network (HNN) is used for finding symptoms. A dataset of CT scans, including 12 pattern images, was trained by this neural network, and 295CT images from three different datasets were tested via the model.
Results: The sensitivity and specificity of the model for detecting COVID-19 in test data were 97.4% (149 of 153) and 98.6% (140 of 142) respectively. Also, the sensitivity and specificity of the model for detecting CAP (community acquired pneumonia) in test data were 97.3% (106 of 109) and 99.5% (185 of 186) respectively, and, the sensitivity and specificity of the model for detecting non-pneumonia patients were 100% (33 of 33) and 98.5% (258 of 262) respectively.
Conclusion: This new algorithm can potentially be helpful for detecting the novel Coronavirus patients using CT images.
Keywords: COVID-19; Coronavirus disease 2019; Hopfield Neural Network; Image Processing; Machine Learning; Operation Research.
. 2022 Feb 24;116740.
doi: 10.1016/j.eswa.2022.116740. Online ahead of print.
A novel algorithm for detection of COVID-19 by analysis of chest CT images using Hopfield neural network
Saeed Sani[SUP] 1 [/SUP], Hossein Ebrahimzadeh Shermeh[SUP] 2 [/SUP]
Affiliations
- PMID: 35228781
- PMCID: PMC8867982
- DOI: 10.1016/j.eswa.2022.116740
Abstract
Background: Widely spread of COVID-19 virus has put the whole world in jeopardy. At this moment, using new techniques to detect and treat this novel disease is of significance or may be the first priority of many scientists and researchers through the world.
Purpose: To present a new algorithm for detection of the novel coronavirus 2019 using chest CT images with high accuracy.
Materials and methods: In this study, we looked at the newly-presented data and detection methods of this disease using chest CT; then, a new neural network algorithm was presented to recognize the COVID-19 symptoms. A mathematical model is used to enhance the accuracy of masking, and a high accuracy Hopfield Neural Network (HNN) is used for finding symptoms. A dataset of CT scans, including 12 pattern images, was trained by this neural network, and 295CT images from three different datasets were tested via the model.
Results: The sensitivity and specificity of the model for detecting COVID-19 in test data were 97.4% (149 of 153) and 98.6% (140 of 142) respectively. Also, the sensitivity and specificity of the model for detecting CAP (community acquired pneumonia) in test data were 97.3% (106 of 109) and 99.5% (185 of 186) respectively, and, the sensitivity and specificity of the model for detecting non-pneumonia patients were 100% (33 of 33) and 98.5% (258 of 262) respectively.
Conclusion: This new algorithm can potentially be helpful for detecting the novel Coronavirus patients using CT images.
Keywords: COVID-19; Coronavirus disease 2019; Hopfield Neural Network; Image Processing; Machine Learning; Operation Research.