Analysis of COVID-19 Infections on a CT Image Using DeepSense Model

被引:22
|
作者
Khadidos, Adil [1 ]
Khadidos, Alaa O. [2 ]
Kannan, Srihari [3 ]
Natarajan, Yuvaraj [4 ]
Mohanty, Sachi Nandan [5 ]
Tsaramirsis, Georgios [6 ]
机构
[1] King Abdulaziz Univ, Fac Comp & Informat Technol, Dept Informat Technol, Jeddah, Saudi Arabia
[2] King Abdulaziz Univ, Fac Comp & Informat Technol, Dept Informat Syst, Jeddah, Saudi Arabia
[3] SNS Coll Engn, Dept Comp Sci & Engn, Coimbatore, Tamil Nadu, India
[4] Informat Commun Technol Acad, Res & Dev, Chennai, Tamil Nadu, India
[5] Inst Chartered Financial Analysts India Fdn Highe, Dept Comp Sci & Engn, Hyderabad, India
[6] Womens Coll, Higher Coll Technol, Abu Dhabi, U Arab Emirates
关键词
DeepSense; artificial intelligence; convolutional neural network; CT images; prediction; COVID-19; INTERNET;
D O I
10.3389/fpubh.2020.599550
中图分类号
R1 [预防医学、卫生学];
学科分类号
1004 ; 120402 ;
摘要
In this paper, a data mining model on a hybrid deep learning framework is designed to diagnose the medical conditions of patients infected with the coronavirus disease 2019 (COVID-19) virus. The hybrid deep learning model is designed as a combination of convolutional neural network (CNN) and recurrent neural network (RNN) and named as DeepSense method. It is designed as a series of layers to extract and classify the related features of COVID-19 infections from the lungs. The computerized tomography image is used as an input data, and hence, the classifier is designed to ease the process of classification on learning the multidimensional input data using the Expert Hidden layers. The validation of the model is conducted against the medical image datasets to predict the infections using deep learning classifiers. The results show that the DeepSense classifier offers accuracy in an improved manner than the conventional deep and machine learning classifiers. The proposed method is validated against three different datasets, where the training data are compared with 70%, 80%, and 90% training data. It specifically provides the quality of the diagnostic method adopted for the prediction of COVID-19 infections in a patient.
引用
收藏
页数:9
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