Deep Ensemble Learning-Based Models for Diagnosis of COVID-19 from Chest CT Images

Abstract

Novel coronavirus (COVID-19) has been endangering human health and life since 2019. The timely quarantine, diagnosis, and treatment of infected people are the most necessary and important work. The most widely used method of detecting COVID-19 is real-time polymerase chain reaction (RT-PCR). Along with RT-PCR, computed tomography (CT) has become a vital technique in diagnosing and managing COVID-19 patients. COVID-19 reveals a number of radiological signatures that can be easily recognized through chest CT. These signatures must be analyzed by radiologists. It is, however, an error-prone and time-consuming process. Deep Learning-based methods can be used to perform automatic chest CT analysis, which may shorten the analysis time. The aim of this study is to design a robust and rapid medical recognition system to identify positive cases in chest CT images using three Ensemble Learning-based models. There are several techniques in Deep Learning for developing a detection system. In this paper, we employed Transfer Learning. With this technique, we can apply the knowledge obtained from a pre-trained Convolutional Neural Network (CNN) to a different but related task. In order to ensure the robustness of the proposed system for identifying positive cases in chest CT images, we used two Ensemble Learning methods namely Stacking and Weighted Average Ensemble (WAE) to combine the performances of three fine-tuned Base-Learners (VGG19, ResNet50, and DenseNet201). For Stacking, we explored 2-Levels and 3-Levels Stacking. The three generated Ensemble Learning-based models were trained on two chest CT datasets. A variety of common evaluation measures (accuracy, recall, precision, and F1-score) are used to perform a comparative analysis of each method. The experimental results show that the WAE method provides the most reliable performance, achieving a high recall value which is a desirable outcome in medical applications as it poses a greater risk if a true infected patient is not identified.

Evaluation of stacked ensemble model performance to predict clinical outcomes: A COVID-19 study

“…The ML methodology for clinical data analysis proposed here is complementary with the application of ensemble methods to other types of patient datasets, such as imaging (e.g., [45] , [46] , [47] ). A more accurate evaluation of ensemble method performance using time varying or image based COVID-19 patient data such as hospitalization duration or CT (computed tomography) scans could pave the way for improved interpretation of ensemble behavior on datasets with similar clinical features and modalities.…”

A Comprehensive Review of Machine Learning Used to Combat COVID-19

“…Research in [ 104 ] combines the use of Stacking and Weighted Average Ensemble (WAE) on popular models (VGG19, ResNet50, and DenseNet201) in an ensemble learning approach to diagnosing COVID-19. The input is passed to the fine-tuned popular models mentioned before, and the output is passed to Random Forest and Extra Trees classifiers.…”

COVLIAS 3.0 XEDL : Multicentre, Cloud-Based, Explainable Ensemble Artificial Intelligence Deep Learning System for COVID- 19 in Computed Tomography Scans

“…Although transfer learning (TL) models are effective, their inability to combine them has forced the use of ensemble methods [4][5][6][7]. Ensemble deep learning (EDL) is a powerful technique for improving prediction accuracy by combining the results of multiple machine learning [8,9] and deep learning models [6,[10][11][12][13]. Earlier EDL methods were focused on classification, but statistical analysis, heatmaps to detect COVID lesions and model validations were neglected [14][15][16][17][18].…”