ABSTRACT
Coronavirus (COVID-19) is an air-borne disease that has affected the lifestyle of people all around the world. Tracing patients infected with coronavirus has become a difficult process because of the limitation of tests based on reverse transcription-polymerase chain reaction (RT-PCR). Recently, methodologies based on imaging have been proposed by various researchers especially using deep learning-based models for the detection of COVID infection. This paper analyzes the effectiveness of deep features for COVID detection from CT scan images. Deep features were extracted from the final layers of deep learning models which are then fed into machine learning frameworks for classification. Transfer learned features obtained from ResNet50, Inception V3, and EfficientNetB7 were employed for the study. A combination of Inception V3 and SVM gave the best accuracy of 86.12 and precision and recall with 83.11 and 80.44, respectively. These results are comparable to recent transfer learning approaches and architecture that is about to be discussed is having an advantage of minimized time when compared to traditional deep learning approaches. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.