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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 | ||
| مراجع | ||
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