An Automated Classification Technique for COVID-19 Using Optimized Deep Learning Features

Ejaz Khan, Muhammad Zia Ur Rehman, Fawad Ahmed, Suliman A. Alsuhibany, Muhammad Zulfiqar Ali, Jawad Ahmad

Abstract: In 2020, COVID-19 started spreading throughout the world. This deadly infection was identified as a virus that may affect the lungs and, in severe cases, could be the cause of death. The polymerase chain reaction (PCR) test is commonly used to detect this virus through the nasal passage or throat. However, the PCR test exposes health workers to this deadly virus. To limit human exposure while detecting COVID-19, image processing techniques using deep learning have been successfully applied. In this paper, a strategy based on deep learning is employed to classify the COVID-19 virus. To extract features, two deep learning models have been used, the DenseNet201 and the SqueezeNet. Transfer learning is used in feature extraction, and models are fine-tuned. A publicly available computerized tomography (CT) scan dataset has been used in this study. The extracted features from the deep learning models are optimized using the Ant Colony Optimization algorithm. The proposed technique is validated through multiple evaluation parameters. Several classifiers have been employed to classify the optimized features. The cubic support vector machine (Cubic SVM) classifier shows superiority over other commonly used classifiers and attained an accuracy of 98.72%. The proposed technique achieves state-of-the-art accuracy, a sensitivity of 98.80%, and a specificity of 96.64%.

External IDs:dblp:journals/csse/KhanRAAAA23