A novel hybrid heuristic adopted ensemble of deep learning models for COVID-19 detection framework using CT and X-Ray images.

In 2019, Corona Virus Disease (COVID)-19 has created an important impact on people's health and economy because of its rapid spreading. Therefore, the earlier detection and diagnosis for handling the further spread is regarded as a significant factor. Computed Tomography (CT) images of the lungs are utilised for the detection process of COVID-19 infection. Chest X-Ray (CXR) and CT serves as the major source for the identification of COVID-19. Some of the classifiers have shown promising outcomes as well as better performance over various medical imaging applications and computer vision processes. Due COVID pandemic, researchers have utilised standard techniques for accurately identifying the coronavirus infection in lung images. Here, a new COVID-19 detection approach has been implemented with the adoption of ensemble classifiers to recognise the occurrence of COVID-19 infection at the starting phase. The required CT and the CXR images are garnered from publicly presented online sources. Meanwhile, the acquired images undergo the process of pre-processing using filtering methods and histogram equalisation techniques in order to ignore the unnecessary artifacts are presented in the images. Then, the detection of COVID-19 is made possible through Ensemble Classifiers (EC) such as Multi-scale Convolutional Neural Networks (CNN), Residual Attention Networks (RAN), Visual Geometry Group (VGG), and Long Short Term Memory (LSTM), where the variables within the machine learning strategies are tuned by Modified Mexican Axolotl Shuffled Frog-Leaping Optimization (MMASFLO). The efficacy of the newly developed ensemble classifier-based COVID-19 detection approaches will be tested through various benchmark datasets, and is tested with several existing approaches and conventional COVID-19 detection approaches. [ABSTRACT FROM AUTHOR]