karimiyan abdar, abolfazl, sadjadi, seyyed mostafa, bashirgonbadi, ali, naghibi, mehran, Soltanian-Zadeh, Hamid. (1401). Extended VGG16 Deep-Learning Detects COVID-19 from Chest CT Images. نشریه مهندسی عمران امیرکبیر, 54(1), 79-90. doi: 10.22060/eej.2021.20264.5426
abolfazl karimiyan abdar; seyyed mostafa sadjadi; ali bashirgonbadi; mehran naghibi; Hamid Soltanian-Zadeh. "Extended VGG16 Deep-Learning Detects COVID-19 from Chest CT Images". نشریه مهندسی عمران امیرکبیر, 54, 1, 1401, 79-90. doi: 10.22060/eej.2021.20264.5426
karimiyan abdar, abolfazl, sadjadi, seyyed mostafa, bashirgonbadi, ali, naghibi, mehran, Soltanian-Zadeh, Hamid. (1401). 'Extended VGG16 Deep-Learning Detects COVID-19 from Chest CT Images', نشریه مهندسی عمران امیرکبیر, 54(1), pp. 79-90. doi: 10.22060/eej.2021.20264.5426
karimiyan abdar, abolfazl, sadjadi, seyyed mostafa, bashirgonbadi, ali, naghibi, mehran, Soltanian-Zadeh, Hamid. Extended VGG16 Deep-Learning Detects COVID-19 from Chest CT Images. نشریه مهندسی عمران امیرکبیر, 1401; 54(1): 79-90. doi: 10.22060/eej.2021.20264.5426

Extended VGG16 Deep-Learning Detects COVID-19 from Chest CT Images

AUT Journal of Electrical Engineering
مقاله 7، دوره 54، شماره 1، شهریور 2022، صفحه 79-90    اصل مقاله (1.39 M)
نوع مقاله: Research Article
شناسه دیجیتال (DOI): 10.22060/eej.2021.20264.5426
نویسندگان
abolfazl karimiyan abdar1؛ seyyed mostafa sadjadi1؛ ali bashirgonbadi1؛ mehran naghibi2؛ Hamid Soltanian-Zadeh* 3
1CIPCE, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
2Department of Anatomical Sciences, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran
3CIPCE, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran- Departments of Radiology and Research Administration, Henry Ford Health System, Detroit, MI, USA
چکیده
Coronavirus disease 2019 (COVID-19), is a rapidly spreading disease that has infected millions of people worldwide. One of the essential steps to prevent spreading COVID-19 is an effective screening of infected individuals. In addition to clinical tests like Reverse Transcription-Polymerase Chain Reaction (RT-PCR), medical imaging techniques such as Computed Tomography (CT) can be used as a rapid technique to detect and evaluate patients infected by COVID-19. Conventionally, CT-based COVID-19 detection is performed by an expert radiologist. In this paper, we will completely and utterly discuss COVID-19. We present a deep learning Convolutional Neural Network (CNN) model that we have developed to detect chest CT images with COVID-19 lesions. Afterwards, based on the fact that in an infected individual, more than one slice is involved, we determine and apply the best threshold to detect COVID-19 positive patients. We collected 5,225 CT images from 130 COVID-19 positive patients and 4,955 CT images from 130 healthy subjects. We used 3,684 CT images with COVID-19 lesions and their corresponding slices from healthy control subjects to build our model. We used 5-fold-cross-validation to evaluate the model, in which each fold contains 26 patients and 26 healthy subjects. We obtained a sensitivity of 91.5%±6.8%, a specificity of 94.6%±3.4%, an accuracy of 93.0%±3.9%, a precision of 94.5%±3.5%, and an F1-Score of 0.93±0.04.
کلیدواژه‌ها
Artificial Intelligence؛ Neural Networks؛ Image Processing؛ COVID-19؛ Diagnosis
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