Title of article :
COVID-19 Detection from X-ray Images Using Different Artificial Intelligence Hybrid Models
Author/Authors :
alqudah, ali mohammad yarmouk university - department of biomedical systems and informatics engineering, Irbid, Jordan , qazan, shoroq yarmouk university - department of computer engineering, Irbid, Jordan , alquran, hiam yarmouk university - department of biomedical systems and informatics engineering, Irbid, Jordan , abu qasmieh, isam yarmouk university - department of biomedical systems and informatics engineering, Irbid, Jordan , alqudah, amin yarmouk university - department of computer engineering, Irbid, Jordan
From page :
168
To page :
178
Abstract :
COVID-19 leads to severe respiratory symptoms that are associated with highly intensive care unit (ICU) admissions and deaths. Early diagnosis of coronavirus limits its wide spreading. Real-time reverse transcription-polymerase chain reaction (RT-PCR) is the strategy that has been used by clinicians to discover the presence or absence of this type of virus. This technique has a relatively low positive rate in the early stage of this disease. Therefore, clinicians call for another way to help in the diagnosis of COVID-19. The appearance of X-ray chest images in case of COVID-19 is different from any other type of pneumonic disease. Therefore, this research is devoted to employ artificial intelligence techniques in the early detection of COVID-19 from chest X-ray images. Different hybrid models – each consists of deep features’ extraction and classification technique - are implemented to assist clinicians in the detection of COVID-19. Convolutional neural network (CNN) is used to extract the graphical features in the hybrid models’ implementations from the chest X-ray images. The classification, to COVID-19 or Non-COVID-19, is achieved using different machine learning algorithms such as CNN, support vector machine (SVM), and random forest (RF) to obtain the best recognition performance. The most significant two extracted features are employed for training and parameters testing. According to the performance results of the designed models, CNN outperforms other classifiers with a testing accuracy of 95.2%.
Keywords :
COVID , 19 , Chest X , ray images , Convolutional neural network , Support vector machine, Random forest , Deep learning , Machine learning , Artificial intelligence
Journal title :
Jordan Journal Of Electrical Engineering
Journal title :
Jordan Journal Of Electrical Engineering
Record number :
2643097
Link To Document :
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