Computer-Aided COVID-19 Screening from Chest CT-Scan using a Fuzzy Ensemble-based Technique

ABSTRACT

The worldwide breakout of the novel COVID-19 has resulted in one of the worst epidemics in modern times since World War II. Although various vaccinations are being produced, their efficacy remains a considerable hurdle. This is especially true when new virus strains emerge. The main challenge to combating this pandemic is diagnosing and isolating COVID-19 positive cases as early as possible. As a result, COVID-19 needs to be detected early and accurately to prevent its spread. This paper proposes a computer-aided automated COVID-19 detection tool based on Computed Tomography (CT-scan) images of lungs. The proposed approach applies an ensemble technique based on Sugeno Fuzzy Integrals with convolutional neural networks (CNNs) as the base model. The lack of COVID-19 data makes it challenging to train a standard CNN from scratch, so we use a transfer learning approach instead of training the base classifiers, VGG-16, InceptionResnetV2, and Xception. We apply the gained knowledge in the target domain of small CT-scan data, considering ImageNet dataset as the source domain. We have also adapted image pre-processing techniques to remove noises so that the model can only focus on specific features. Our proposed framework achieves 98.99% accuracy on a publicly available dataset and outperforms the existing state-of-the-art methods. Experimental results and comparative analysis with baselines establish the need and effectiveness of our proposed model. © 2022 IEEE.