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In International journal of computer assisted radiology and surgery

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.

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


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