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Optimization Techniques for Influenza Prediction in Biological Expert Systems
Abstract
Currently, the biggest challenge in the world is the detection of viral infection in various diseases, as par to the rapid spread of the disease. According to recent statistics, the number of people diagnosed with the Influenza virus is exponentially increasing day by day, with more than 2.5 million confirmed cases. The model proposed here analyses the Influenza virus by comparing different deep learning algorithms to bring out the best in terms of accuracy for detection and prediction. The models are trained using CT scan dataset comprising of both Influenza positive patients and negative patients. The results of algorithms are compared based on parameters such as train accuracy, test loss, etc. Some of the best models after training were, DenseNet-121 with accuracy of 96.28%, VGG-16 with accuracy of 95.75%, ResNet-50 with accuracy of 94.18%, etc. in detecting the virus from the CT scan dataset with the proposed ACDL algorithm. Thus, these models will be helpful and useful to the government and communities to initiate proper measures to control the outbreak of the Influenza virus in time.
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