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

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. Supplementary Information The online version contains supplementary material available at 10.1007/s11548-021-02317-0.

A Novel Weighted Consensus Machine Learning Model for COVID-19 Infection Classification Using CT Scan Images

“…Maior et al [38] performed an analysis on chest X-ray images combining six different databases from open datasets to determine images of infected patients while distinguishing COVID-19 and pneumonia from 'no-findings' images. Saba et al [39] proposed six models for the tissue characterization and classification of COVID-19 with pneumonia and achieved better results.…”

COVLIAS 1.0: Lung Segmentation in COVID-19 Computed Tomography Scans Using Hybrid Deep Learning Artificial Intelligence Models

“…This makes the task of processing scanned images tedious and time-consuming [10]. The second issue with current automated or semi-automated systems is reliability, accuracy, and clinical effectiveness [11]. One of the major causes for unreliable accuracy and low performance is the intensity-based segmentation methods which are influenced by local or global statistical methods.…”

“…[34,35]. It offers substantial benefits compared to ML-based solutions [11,36]. DL provides a complete automated feature extraction and classification simultaneously using the so-called dense layers.…”

COVLIAS 1.0 vs. MedSeg: Artificial Intelligence-Based Comparative Study for Automated COVID-19 Computed Tomography Lung Segmentation in Italian and Croatian Cohorts

“…As part of the pipeline for COVID-19 diagnosis, CT lung segmentation is crucial [ 1 , 2 , 3 ]. Here is where artificial intelligence (AI) comes into play in automating this time-consuming process and providing a faster diagnosis of the disease [ 22 , 23 , 24 , 25 ].…”