Leveraging Multimodal Ensemble Fusion-Based Deep Learning for COVID-19 on Chest Radiographs.

Recently, COVID-19 has posed a challenging threat to researchers, scientists, healthcare professionals, and administrations over the globe, from its diagnosis to its treatment. The researchers are making persistent efforts to derive probable solutions for managing the pandemic in their areas. One of the widespread and effective ways to detect COVID-19 is to utilize radiological images comprising X-rays and computed tomography (CT) scans. At the same time, the recent advances in machine learning (ML) and deep learning (DL) models show promising results in medical imaging. Particularly, the convolutional neural network (CNN) model can be applied to identifying abnormalities on chest radiographs. While the epidemic of COVID-19, much research is led on processing the data compared with DL techniques, particularly CNN. This study develops an improved fruit fly optimization with a deep learning-enabled fusion (IFFO-DLEF) model for COVID-19 detection and classification. The major intention of the IFFO-DLEF model is to investigate the presence or absence of COVID-19. To do so, the presented IFFO-DLEF model applies image pre-processing at the initial stage. In addition, the ensemble of three DL models such as DenseNet169, EfficientNet, and ResNet50, are used for feature extraction. Moreover, the IFFO algorithm with a multilayer perceptron (MLP) classification model is utilized to identify and classify COVID-19. The parameter optimization of the MLP approach utilizing the IFFO technique helps in accomplishing enhanced classification performance. The experimental result analysis of the IFFO-DLEF model carried out on the CXR image database portrayed the better performance of the presented IFFO-DLEF model over recent approaches.