A Deep Learning-based Radiomics Approach for COVID-19 Detection from CXR Images using Ensemble Learning Model

• Main Authors: Costa, Marcus V. L., de Aguiar, Erikson J., Rodrigues, Lucas S., Ramos, Jonathan S., Traina, Caetano, Traina, Agma J. M.
• Format: Conference Proceeding
• Language: English
• Subjects: Adaptation models; Classification algorithms; COVID-19; Deep Features; Deep Learning; Deep Radiomics; Feature extraction; Medical Images; Radiomics; Task analysis; Transfer learning; Visualization
• ISSN: 2372-9198
• DOI: 10.1109/CBMS58004.2023.00272

Medical image analysis plays a major role in aiding physicians in decision-making. Specifically in detecting COVID-19, Deep Learning (DL) and radiomic approaches have achieved promising results separately. However, DL results are hard to interpret/visualize, and the radiomic approach encompasses successive steps, such as image acquisition, image processing, segmentation, feature extraction, and analysis. In this paper, we integrate DL with radiomic approaches, aiding in detecting COVID-19. We use DL models to extract 128 relevant deep radiomic features to assess COVID-19 from several image sources of 392 representative chest X-ray (CXR) exams. We avoid successive radiomic steps by employing DL (transfer learning) from Imagenet's VGG-16, ResNet50V2, and DenseNet201 networks. We considered a set of Machine Learning (ML) algorithms to further validate our results, providing an ensemble model to detect COVID-19. Our experimental results show that our approach achieved 95% AUC using 128 relevant features from DenseNet201. Conversely, our ensemble model presented 91% AUC, indicating that deep learning-based radiomics could increase binary classification performance in a real scenario. In addition, we highlight that our approach can be adapted to create other DL-based radiomics tools. For reproducibility, we made our code available at https://github.com/usmarcv/CBMS-DL-based-radiomics.