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Deep Convolutional Neural Network-Based Computer-Aided Detection System for COVID-19 Using Multiple Lung Scans: Design and Implementation Study.
Ghaderzadeh, Mustafa; Asadi, Farkhondeh; Jafari, Ramezan; Bashash, Davood; Abolghasemi, Hassan; Aria, Mehrad.
  • Ghaderzadeh M; Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
  • Asadi F; Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
  • Jafari R; Department of Radiology, Baqiyatallah University of Medical Sciences, Tehran, Iran.
  • Bashash D; Department of Hematology and Blood Banking, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
  • Abolghasemi H; Pediatric Congenital Hematologic Disorders Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
  • Aria M; Department of Computer Engineering, Faculty of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran.
J Med Internet Res ; 23(4): e27468, 2021 04 26.
Article in English | MEDLINE | ID: covidwho-1219288
ABSTRACT

BACKGROUND:

Owing to the COVID-19 pandemic and the imminent collapse of health care systems following the exhaustion of financial, hospital, and medicinal resources, the World Health Organization changed the alert level of the COVID-19 pandemic from high to very high. Meanwhile, more cost-effective and precise COVID-19 detection methods are being preferred worldwide.

OBJECTIVE:

Machine vision-based COVID-19 detection methods, especially deep learning as a diagnostic method in the early stages of the pandemic, have been assigned great importance during the pandemic. This study aimed to design a highly efficient computer-aided detection (CAD) system for COVID-19 by using a neural search architecture network (NASNet)-based algorithm.

METHODS:

NASNet, a state-of-the-art pretrained convolutional neural network for image feature extraction, was adopted to identify patients with COVID-19 in their early stages of the disease. A local data set, comprising 10,153 computed tomography scans of 190 patients with and 59 without COVID-19 was used.

RESULTS:

After fitting on the training data set, hyperparameter tuning, and topological alterations of the classifier block, the proposed NASNet-based model was evaluated on the test data set and yielded remarkable results. The proposed model's performance achieved a detection sensitivity, specificity, and accuracy of 0.999, 0.986, and 0.996, respectively.

CONCLUSIONS:

The proposed model achieved acceptable results in the categorization of 2 data classes. Therefore, a CAD system was designed on the basis of this model for COVID-19 detection using multiple lung computed tomography scans. The system differentiated all COVID-19 cases from non-COVID-19 ones without any error in the application phase. Overall, the proposed deep learning-based CAD system can greatly help radiologists detect COVID-19 in its early stages. During the COVID-19 pandemic, the use of a CAD system as a screening tool would accelerate disease detection and prevent the loss of health care resources.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Diagnosis, Computer-Assisted / Deep Learning / SARS-CoV-2 / COVID-19 / Lung Type of study: Diagnostic study / Experimental Studies Limits: Humans Language: English Journal: J Med Internet Res Journal subject: Medical Informatics Year: 2021 Document Type: Article Affiliation country: 27468

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Diagnosis, Computer-Assisted / Deep Learning / SARS-CoV-2 / COVID-19 / Lung Type of study: Diagnostic study / Experimental Studies Limits: Humans Language: English Journal: J Med Internet Res Journal subject: Medical Informatics Year: 2021 Document Type: Article Affiliation country: 27468