Lung Segmentation followed by Machine Learning & Deep Learning Techniques for COVID-19 Detection in lung CT Images

ABSTRACT

In the light of the rapidly growing COVID-19 pandemic, the need for an expeditious diagnosis of COVID-19 infection became essential. The immediate diagnosis will allow the initiation of the isolation process and adequate treatment as well. While the standard test used for the diagnosis of COVID19 disease (RT-PCR) is usually time consuming (6 hours up to days in some centers);the need for a highly sensitive test became essential. Many studies have illustrated the utility of chest CT scan in the diagnoses of COVID-19. This paper evaluates the value of classical machine learning techniques and the convolutional neural networks in aiding physicians to further classify patients into either COVID-19 positive or negative according to their chest CT findings, and thus facilitating their work. To address this problem, this paper proposes classical neural networks using statistical features and deep CNN models to further classify a dataset of preprocessed chest CT images, using several classifiers and to evaluate the results. This latter showed that the best proposed method was a four layers CNN with SVM classifier with 99.6% accuracy. This demonstrates the potential of the proposed technique in computer-aided diagnosis for healthcare applications, especially for COVID-19 classification.