FADCOVNET: Fast Automatic Detection Covid-19 Based on Inception-Resnet-V2 Model

Abstract

Covid-19 is still a threat to human health. Initial handling in detecting the status of positive COVID-19 patients or not through the IT sector is still very much needed to help the government control the covid-19 outbreak. This study offers a new framework of deep learning classification to help radiologists work in auto-detecting cases of COVID-19 by processing patient X-Ray chest (we call it FADCOVNET). By combining pre-processing techniques with a modified Inception Resnet V2 trained network on the Fully Connected layer and by adding pre-processing data. To control overfitting, the data augmentation method is used. The FADCOVNET model will be compared with the transfer learning model (Resnet50, Inception V3, Inception-Resnet-V3).The dataset used in this study is chest X-ray data for COVID cases as many as 4369 total data. In addition, this study also tested the performance of FADCOVNET on the Covid and healthy chest CT-Scan dataset of 8467 total data. The test results show that the performance of FADCOVNET on the accuracy, sensitivity, specification, precision, and F1-Score are 97%, 98%, 97%, 95%, and 96%, respectively. The results obtained outperform other transfer models. while the accuracy obtained from testing with the CT Scan dataset is 97%. This proves that the FADCOVNET model that we have built can ensure the generalizability of the model very well. From this test, it can be concluded that the proposed CNN architecture works very well in detecting COVID-19.

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