ABSTRACT
Coronavirus Disease 2019 (COVID-19) is a viral pneumonia that causes symptoms in the lungs of those infected. The presence of the symptoms must be diagnosed as soon as possible. If no test kits are available, the next best alternative is a computer-aided diagnostic of a patient's chest X-ray scan for a quick and accurate diagnosis. This paper proposes a hybrid transfer learning method with Error-Correction Output Codes (ECOC) by combining networks including GoogLeNet, ResNet-18, and ShuffleNet for feature extraction. X-ray input data are collected from open-source repositories. In this implementations, Support Vector Machine (SVM) as the base classifier. The proposed network attempts to categorize the input data into one of three categories: COVID-19, healthy, and non-COVID-19 pneumonia. The mean accuracy of our method is 96.21%, compared fine tuning existing pre-trained model which yielded 89.1% for GoogLeNet, 88.95% for ResNet-18, and 89.31% for ShuffleNet. © 2022 IEEE.