Discriminating the Pneumonia-Positive Images from COVID-19-Positive Images Using an Integrated Convolutional Neural Network
D. Vetrithangam, V. Indira, Syed Umar, Bhaskar Pant, Mayank Kumar Goyal, B. Arunadevi
Abstract
One of the most pressing issues in the current COVID-19 pandemic is the early detection and diagnosis of COVID-19, as well as the precise separation of non-COVID-19 cases at the lowest possible cost and during the disease's early stages. Deep learning-based models have the potential to provide an accurate and efficient approach for the identification and diagnosis of COVID-19, with considerable increases in sensitivity, specificity, and accuracy when used in the processing of modalities. COVID-19 illness is difficult to detect and recognize since it is comparable to pneumonia. The main objective of this study is to distinguish between COVID-19-positive images and pneumonia-positive images. We have proposed an integrated convolutional neural network focused on discriminating against COVID-19-infected patients and pneumonia patients. Preprocessing is done on the image datasets. The novelty of this research work is to differentiate the COVID-19 images from the pneumonia images. It will help the medical experts in the decision-making. In order to train the model, the image is given directly as input to integrated convolutional neural network architecture; after training the model, the system is integrated with three different kinds of datasets: COVID-19 image dataset, RSNA pneumonia dataset, and a new dataset created from COVID-19 image dataset. The attainment of the system is evaluated by calculating the measures of sensitivity, specificity, precision, and accuracy, and this system produces the accuracy values of 94.04%, 97.2%, and 97.5% for the above datasets, respectively.