Efficient deep transfer learning based COVID-19 detection and classification using CT images Online publication date: Fri, 01-Mar-2024
by G. Prabakaran; K. Jayanthi
International Journal of System of Systems Engineering (IJSSE), Vol. 14, No. 2, 2024
Abstract: This paper develops an intelligent deep transfer learning-driven COVID-19 detection and classification model using CT images. The major aim of the IDTLD-CDCM model is to identify appropriate class labels for the CT images. The IDTLD-CDCM model undergoes initial pre-processing in two levels namely spline adaptive filtering (SAF) based noise removal and contrast enhancement. In addition, the IDTLD-CDCM model involves SqueezeNet as a feature extractor for deriving a useful set of feature vectors. Furthermore, the hop field neural network (HFNN) model is utilised for the classifier of COVID-19 and Non-COVID-19 images. Furthermore, the parameter tuning of the HFNN model is carried out by the use of root mean square propagation (RMSProp). To investigate the improved outcomes of the IDTLD-CDCM approach, a series of simulations are executed and the outcomes are inspected in several aspects. The simulation outcome demonstrated the improved outcomes of the IDTLD-CDCM approach over the recent approaches.
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