Abstract
Background: COVID-19 pandemic has currently no vaccines. Thus, the only feasible solution for prevention relies on the detection of COVID-19-positive cases through quick and accurate testing. Since artificial intelligence (AI) offers the powerful mechanism to automatically extract the tissue features and characterise the disease, we therefore hypothesise that AI-based strategies can provide quick detection and classification, especially for radiological computed tomography (CT) lung scans.
Methodology: Six models, two traditional machine learning (ML)-based (k-NN and RF), two transfer learning (TL)-based (VGG19 and InceptionV3), and the last two were our custom-designed deep learning (DL) models (CNN and iCNN), were developed for classification between COVID pneumonia (CoP) and non-COVID pneumonia (NCoP). K10 cross-validation (90% training: 10% testing) protocol on an Italian cohort of 100 CoP and 30 NCoP patients was used for performance evaluation and bispectrum analysis for CT lung characterisation.
Results: Using K10 protocol, our results showed the accuracy in the order of DL > TL > ML, ranging the six accuracies for k-NN, RF, VGG19, IV3, CNN, iCNN as 74.58 ± 2.44%, 96.84 ± 2.6, 94.84 ± 2.85%, 99.53 ± 0.75%, 99.53 ± 1.05%, and 99.69 ± 0.66%, respectively. The corresponding AUCs were 0.74, 0.94, 0.96, 0.99, 0.99, and 0.99 (p-values < 0.0001), respectively. Our Bispectrum-based characterisation system suggested CoP can be separated against NCoP using AI models. COVID risk severity stratification also showed a high correlation of 0.7270 (p < 0.0001) with clinical scores such as ground-glass opacities (GGO), further validating our AI models.
Conclusions: We prove our hypothesis by demonstrating that all the six AI models successfully classified CoP against NCoP due to the strong presence of contrasting features such as ground-glass opacities (GGO), consolidations, and pleural effusion in CoP patients. Further, our online system takes < 2 s for inference.
Keywords: Accuracy; Bispectrum; COVID-19; Computer tomography; Deep learning; Ground-glass opacities; Lung; Machine learning; Pandemic; Performance; Transfer learning; Validation.
【저자키워드】 COVID-19, pandemic, deep learning, computer tomography, machine learning, lung, validation, Ground-glass opacities, Accuracy, transfer learning, performance, Bispectrum, 【초록키워드】 Stratification, protocol, deep learning, Vaccines, Pneumonia, computer tomography, COVID-19 pandemic, severity, artificial intelligence, machine learning, lung, risk, classification, COVID, Computed tomography, CNN, ground-glass opacity, Cohort, validation, Ground-glass opacities, Patient, GGO, COVID pneumonia, patients, mechanism, Hypothesis, Analysis, Bispectrum, AUC, pleural effusion, Inference, tissue, consolidations, high correlation, computer, transfer, characterisation, p-values, machine, offer, Italian, feature, deep, radiological, was used, the disease, suggested, feasible, separated, automatically, hypothesise, 【제목키워드】 Pneumonia, Non-COVID-19, tissue, characterisation,