Deep learning based classification of COVID-19 severity using hierarchical deep maxout model

COVID-19 is a huge pandemic disease and is gradually growing into our daily lives. It has affected us all in different ways, which the World Health Organization classified as a pandemic in 2020. A number of deep learning methods have been developed to identify COVID-19 in chest X-rays, but very few techniques are used to estimate the severity of COVID-19. Additionally, it is very complex to effectively train the data, resulting in slower convergence and time complexities. Therefore, to overcome these drawbacks, this study proposes a unique model to classify the severity of Covid-19 using a deep learning method. The pre-processing step includes image enhancement and noise reduction using the Amended Bilateral Filter (ABF). The relevant features are then extracted using an ensemble-based deep learning model, which includes the DenseNet-201, ResNet-152 and VGG-19 models, which are integrated via an ensemble average approach. The most optimal features are selected using the Modified Archer Fish Optimization Algorithm (Mod-AFO). The proposed optimization algorithm exhibits high convergence and reduced model complexity. COVID-19 severity classification can be effectively categorized using a Multihead attention-assisted hierarchical deep-maxout network model (Multi-HDM). The proposed classification model is comparatively faster in severity estimation and high in performance. Using Python, the proposed model performances are analyzed and compared with the existing models to demonstrate the superiority of the proposed model. The proposed model achieved 98.2% accuracy, 98.5% sensitivity, 96.4% specificity and 96.4% precision in COVID-19 severity estimation using the COVID-19 Chest X-ray images and Lung masks dataset.