Towards COVID-19 detection and classification using optimal efficient Densenet model on chest X-ray images

COVID-19 detection is mainly based on molecular testing, especially polymerase chain reaction (PCR) analyses, to find the occurrence of the virus's genetic material in respiratory samples. Computed tomography (CT) scans and X-rays are the two chest imaging techniques, which may provide more details on lungs, aiding in the diagnosis. Radiological findings in COVID-19 cases typically involve special features such as bilateral involvement, ground-glass opacities, and consolidation of the lungs. Deep learning (DL) methods, particularly convolutional neural networks (CNNs) are used for analyzing COVID-19 infection. By training these techniques on large datasets of annotated chest X-rays (CXRs), researches have attained superior outcomes in precisely detecting the possible COVID-19 cases. Leveraging artificial intelligence together with radiological expertise holds great potential for facilitating prompt intervention in the ongoing global efforts and improving the efficiency of early detection. This article focuses on the design of mother optimization algorithm with DL-enabled detection and classification (MOA-DLEDC) technique for COVID-19 diagnosis using CXR images. In the MOA-DLEDC technique, the adaptive median filtering (AMF) approach is used to eliminate the existence of noise. In addition, the complex and intrinsic feature patterns are derived from the Efficient DenseNet model. Besides, the hyperparameter optimization of the Efficient DenseNet model takes place using MOA. For the COVID-19 detection process, the MOA-DLEDC technique applies sparse autoencoder (SAE) model. A detailed set of experiments was conducted on the CXR dataset to highlight the promising results of the MOA-DLEDC technique. The extensive results inferred that the used technique gains superior performance over other models with maximum accuracy of 99.59 %.