Amirkabir University of Technology AUT Journal of Electrical Engineering 2588-2910 54 1 2022 06 01 Extended VGG16 Deep-Learning Detects COVID-19 from Chest CT Images 79 90 4550 10.22060/eej.2021.20264.5426 EN Abolfazl Karimiyan Abdar CIPCE, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran Seyyed Mostafa Sadjadi CIPCE, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran Ali Bashirgonbadi CIPCE, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran 0009-0007-3867-101X Mehran Naghibi Department of Anatomical Sciences, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran Hamid Soltanian-Zadeh CIPCE, 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 0000-0002-7302-6856 Journal Article 2021 07 16 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. https://eej.aut.ac.ir/article_4550_62021a18331216014fee6916d6ee9584.pdf