An Ensemble Machine Learning Classifier With Deep Learning Framework for COVID ‐19 Detection and Severity Classification

The influence of the COVID‐19 pandemic on respiratory health is noteworthy, which tends to exacerbate continuous challenges in global healthcare systems. Chest X‐ray (CXR) analysis is considered a significant tool for evaluating the impact of COVID‐19 on the lungs. Anyhow, the prevailing analysis approaches frequently seem to be highly complex or cannot activate diagnosis and classify the disease precisely. To overcome these challenges, a novel multimodal deep learning framework that integrates advanced feature extraction, optimization, and classification approaches is proposed for enhanced detection of COVID‐19 and severity assessment. The proposed framework introduces a novel hybrid spatial attention assisted extended version of the LSTM and GRU model (HAS‐E‐LSTM‐GRU) that integrates long and short‐term temporal feature extraction and attention approach. The prolonged framework maximizes the number of hidden units, whereas the learning and complex patterns obtained from CXR images represent a key novelty of the proposed model. The extracted features are enhanced further using Fourier, Wavelet, Convolutional, Gabor, and Cosine transforms. It is pursued by a feature selection approach using Grey Wolf Optimizer (GWO) to determine the most significant features with higher interclass variance. For the detection of disease, an ensemble classifier integrating Naïve Bayes (NB), k Nearest Neighbors (KNN), Deep Forest (DF), and Support Vector Machine (SVM) is used to determine the COVID‐19 presence or absence. Finally, the disease severity is evaluated using a self‐attention‐based 1D convolutional neural network (CNN) that classifies the diverse cases into mild, moderate, and severe. In association with the standard deep learning methodologies, the proposed architecture obtains a detection accuracy of 98.51% which illustrates the capability in the case of real‐time clinical applications.