Deep hybrid models for CT images to detect COVID-19: A comparison of transfer learning approach

Abstract

The COVID-19 has become a pressing public health concern recently due to its dramatic impact. It spreads quickly, and it is beyond the ability of health staff to detect patients with the disease immediately. However, the ability to diagnose SARS-CoV-2 in a short time is critical for fighting the disease. The primary objective of this study is to develop deep neural networks to diagnose disease in a quick, safe, and cheap way. We classify the cases as normal, COVID-19, and pneumonia. Deep neural networks are developed to perform a three-class classification task. Ten deep learning models are evaluated on a large dataset. Although all DCNNs demonstrated promising potential for classification, hybrid neural networks delivered the most promising outcome with the highest accuracies. The first hybrid model is named MICOVID. The second hybrid model is named VVCOVID. These models are developed through transfer learning by using pre-trained deep learning models. Performance metrics results showed that MICOVID and VVCOVID models have an accuracy of 94% for COVID-19 detection. This is higher than other classification models. These findings suggest that two novel hybrid models that we proposed have great potential to be embedded into computer-aided systems to predict disease in radiology departments.

Keywords

• COVID-19
• deep neural network
• hybrid models
• CNN

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