Optimization Techniques for Influenza Prediction in Biological Expert Systems

2. Related Work

Wang et al. (2020) used transfer learning and model integration based on the COVID-net model by Darwin-AL for identifying the COVID virus from Chest X-RAY images, the methodologies were implemented using ResNet-101 and ResNet-152 neural networks. Rajaraman et al. (2020) used iterative pruning for identifying pulmonary signs of the virus, for this they came up with a CNN and set of pre-trained models that have undergone training and evaluation based on Chest X-rays to learn about feature representations concerning modality. Hassan et al. (2020) came up with COVID-19 prediction system using deep learning on chest X-ray images. Mohammed et al. (2020) came up with a method for organizations in selecting a COVID-19 diagnosis system based on the multi-criteria decision-making (MCDM) method. Ko et al. (2020) used a 2D framework known as the FCO-net model, which is a technique to diagnose the virus from images based on CT and to segregate it from non–COVID-19 diseases and non-pneumonia diseases. Transfer learning is used to create the FCO-Net model with the help of four models which are VGG16, ResNet-50, Inception-v3, and Xception. Alazab et al. (2020) presented an artificial-intelligence technique based on CNN to detect COVID19 patients using CXR as a dataset. Punn et al. (2020) used DNN and RNN with the help of LSTM cells for predicting the number of recovered, confirmed, and death cases worldwide. Mahalle et al. (2020) made use of multimodal data for COVID-19 prediction.

In this study, a single dataset is used to implement (train and test) the necessary deep learning algorithms. A CT scan dataset comprising of Influenza patients and healthy persons is used (746 images in total).