Since December 2019, the pandemic of coronavirus (CorV) is spreading all over the world. CorV is a viral disease that results in ill effects on humans and is recognized as public health concern globally. The objective of the paper is to diagnose and prevent the spread of CorV. Spatio-temporal based fine-tuned deep learning model is used for detecting Corv disease so that the prevention measures could be taken on time. Deep learning is an emerging technique that has an extensive approach to prediction. The proposed system presents a hybrid model using chest X-ray images to early identify the CorV suspected people so that necessary action can be taken timely. The proposed work consists of various deep learning neural network algorithms for the identification of CorV patients. A decision model with enhanced accuracy has been presented for early identification of the suspected CorV patients and time-sensitive decision-making. A SQueezeNet model is used for the classification of the CorV patient. An experiment has been conducted for validation purposes to register an average accuracy of 97.8%. Moreover, the outcomes of statistical parameters are compared with numerous state-of-the-art decision-making models in the current domain for performance assessment.
Keywords: Convolutional neural network; Coronavirus; Deep learning; Transfer learning.
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022.