Intelligent Firefly Algorithm Deep Transfer Learning Based COVID-19 Monitoring System

With the increasing and rapid growth rate of COVID-19 cases, healthcare scheme several developed countries have reached point collapse. An important critical steps in fighting against is powerful screening diseased patients, such a way that positive patient can be treated isolated. A chest radiology image-based diagnosis might benefits over traditional approach. The accomplishment artificial intelligence (AI) based techniques automated diagnoses sector increase cases demanded requirement AI recognition systems. This study develops an Intelligent Firefly Algorithm Deep Transfer Learning Based Monitoring System (IFFA-DTLMS). proposed IFFA-DTLMS model majorly aims at identifying categorizing occurrence COVID19 on radiographs. To attain this, presented primarily applies densely connected networks (DenseNet121) to generate collection feature vectors. In addition, firefly algorithm (FFA) applied for hyper parameter optimization DenseNet121 model. Moreover, autoencoder-long short term memory (AE-LSTM) exploited classification identification COVID19. For ensuring enhanced performance model, wide-ranging experiments were performed results are reviewed under distinctive aspects. experimental value reports betterment recent approaches.