Six artificial intelligence paradigms for tissue characterisation and classification of non-COVID-19 pneumonia against COVID-19 pneumonia in computed tomography lungs

Luca Saba, Mohit Agarwal, Anubhav Patrick, Anudeep Puvvula, Suneet K. Gupta, Alessandro Carriero, John R. Laird, George D. Kitas, Amer M. Johri, Antonella Balestrieri, Zeno Falaschi, Alessio Paschè, Vijay Viswanathan, Ayman El-Baz, Iqbal Alam, Abhinav Jain, Subbaram Naidu, Ronald Oberleitner, Narendra N. Khanna, Arindam BitMostafa Fatemi, Azra Alizad, Jasjit S. Suri

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)423-434
Number of pages12
JournalInternational Journal of Computer Assisted Radiology and Surgery
Volume16
Issue number3
DOIs
StatePublished - Mar 2021

Keywords

  • Accuracy
  • Bispectrum
  • COVID-19
  • Computer tomography
  • Deep learning
  • Ground-glass opacities
  • Lung
  • Machine learning
  • Pandemic
  • Performance
  • Transfer learning
  • Validation

ASJC Scopus subject areas

  • Surgery
  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging
  • Computer Vision and Pattern Recognition
  • Health Informatics
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design

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