Rezaee, Khosro, Badiei, Afsoon, Ghayoumi Zadeh, Hossein, Meshgini, Saeed. (1400). A Hybrid Deep Transfer Learning-based Approach for COVID-19 Classification in Chest X-ray Images. نشریه مهندسی عمران امیرکبیر, 53(2), 223-232. doi: 10.22060/eej.2021.19467.5397
Khosro Rezaee; Afsoon Badiei; Hossein Ghayoumi Zadeh; Saeed Meshgini. "A Hybrid Deep Transfer Learning-based Approach for COVID-19 Classification in Chest X-ray Images". نشریه مهندسی عمران امیرکبیر, 53, 2, 1400, 223-232. doi: 10.22060/eej.2021.19467.5397
Rezaee, Khosro, Badiei, Afsoon, Ghayoumi Zadeh, Hossein, Meshgini, Saeed. (1400). 'A Hybrid Deep Transfer Learning-based Approach for COVID-19 Classification in Chest X-ray Images', نشریه مهندسی عمران امیرکبیر, 53(2), pp. 223-232. doi: 10.22060/eej.2021.19467.5397
Rezaee, Khosro, Badiei, Afsoon, Ghayoumi Zadeh, Hossein, Meshgini, Saeed. A Hybrid Deep Transfer Learning-based Approach for COVID-19 Classification in Chest X-ray Images. نشریه مهندسی عمران امیرکبیر, 1400; 53(2): 223-232. doi: 10.22060/eej.2021.19467.5397

A Hybrid Deep Transfer Learning-based Approach for COVID-19 Classification in Chest X-ray Images

AUT Journal of Electrical Engineering
مقاله 8، دوره 53، شماره 2، اسفند 2021، صفحه 223-232    اصل مقاله (1.94 M)
نوع مقاله: Research Article
شناسه دیجیتال (DOI): 10.22060/eej.2021.19467.5397
نویسندگان
Khosro Rezaee* 1؛ Afsoon Badiei2؛ Hossein Ghayoumi Zadeh3؛ Saeed Meshgini2
1Department of Biomedical Engineering, Meybod University, Meybod, Iran
2Department of Biomedical Engineering, Faculty of Electrical and Computer Engineering, Tabriz University, Tabriz, Iran
3Department of Electrical Engineering, Faculty of Engineering, Vali-e-Asr University of Rafsanjan, Rafsanjan, Iran
چکیده
The COVID-19 pandemic is a severe public health hazard. Hence, proper and early diagnosis is necessary to control the infection progression. We can diagnose this disease by employing a chest X-ray (CXR) screening, which is ordinarily cheaper and less harmful than a Computed Tomography scan (CT scan) and is continuously accessible in small or rustic hospitals. Since the COVID-19 dataset is inadequate and cannot be strictly distinguished from CXR, Deep Transfer Learning (DTL) models can be used to diagnose coronavirus even with access to a small number of images. In this paper, we presented an approach to diagnosis COVID-19 using CXR images based on the concatenated features vector of the three DTL structures and soft-voting feature selection procedure, including Receiver of Curve (ROC), Entropy, and signal-to-noise ratio (SNR) techniques. Our hybrid model reduces the feature vector size and classifies it in optimize manner to improve the decision-making process. A collection of 2,863 CXR images comprising normal, bacterial, viral, and COVID-19 cases were prepared in JPEG format from the Medical Imaging Center of Vasei Hospital, Sabzevar, Iran. The proposed approach obtained an Accuracy of 99.34%, Sensitivity of 99.48%, Specificity of 99.27% while having a far fewer number of trainable parameters in contrast to its counterparts. Compared to the latest similar methods, the diagnosis accuracy has increased from 1.5 to 2.2%. The comparative experiment reveals the advantage of the suggested COVID-19 classification pattern based on DTL over other competing schemes.
کلیدواژه‌ها
COVID-19؛ Chest X-ray؛ Deep transfer learning؛ Convolutional neural network؛ Feature selection
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