TY  - JOUR
AU  - Ghaderzadeh, Mustafa
AU  - Asadi, Farkhondeh
AU  - Jafari, Ramezan
AU  - Bashash, Davood
AU  - Abolghasemi, Hassan
AU  - Aria, Mehrad
PY  - 2021
DA  - 2021/4/26
TI  - Deep Convolutional Neural Network–Based Computer-Aided Detection System for COVID-19 Using Multiple Lung Scans: Design and Implementation Study
JO  - J Med Internet Res
SP  - e27468
VL  - 23
IS  - 4
KW  - artificial intelligence
KW  - classification
KW  - computer-aided detection
KW  - computed tomography scan
KW  - convolutional neural network
KW  - coronavirus
KW  - COVID-19
KW  - deep learning
KW  - machine learning
KW  - machine vision
KW  - model
KW  - pandemic
AB  - 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. 
SN  - 1438-8871
UR  - https://www.jmir.org/2021/4/e27468
UR  - https://doi.org/10.2196/27468
UR  - http://www.ncbi.nlm.nih.gov/pubmed/33848973
DO  - 10.2196/27468
ID  - info:doi/10.2196/27468
ER  -