A hybrid CNN and ensemble model for COVID-19 lung infection detection on chest CT scans.

ABSTRACT: COVID-19 is highly infectious and causes acute respiratory disease. Machine learning (ML) and deep learning (DL) models are vital in detecting disease from computerized chest tomography (CT) scans. The DL models outperformed the ML models. For COVID-19 detection from CT scan images, DL models are used as end-to-end models. Thus, the performance of the model is evaluated for the quality of the extracted feature and classification accuracy. There are four contributions included in this work. First, this research is motivated by studying the quality of the extracted feature from the DL by feeding these extracted to an ML model. In other words, we proposed comparing the end-to-end DL model performance against the approach of using DL for feature extraction and ML for the classification of COVI

SUBMITTER: Akl AA 

PROVIDER: S-EPMC9997961 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature