SARS CovidAID: Automatic detection of SARS CoV-19 cases from CT scan images with pretrained transfer learning model (VGG19, RESNet50 and DenseNet169) architecture

Abstract

The COVID-19 outbreak has presented significant challenges to medical professionals worldwide and underscored the need for accurate and effective detection methods due to its highly contagious nature and potential for explosive community transmission. However, healthcare delivery has been hindered by a lack of testing kits. To address this, deep learning techniques have been utilized to diagnose COVID-19 using CT scans, which have higher sensitivity in detecting early pneumonic changes. However, limited access to large datasets of CT-scan images due to privacy concerns has made developing accurate models difficult. To overcome this, transfer-learning pre-trained models have been employed in this study to automatically detect COVID-19 cases from CT scan images. The proposed methodology utilizes VGG19, RESNet50, and DenseNet169 architectures to classify patients as COVID-19 (positive) or COVID-19 (negative), with DenseNet169 performing the best with an accuracy of 98.5% in predicting COVID-19 binary classification. The model showed no signs of overfitting or underfitting, with a great output curve relative to the training accuracy. The other models, ResNet-50 and VGG-19 showed performance well with an accuracy of 96.7% and 92.7%, respectively. However, VGG-19 had the lowest accuracy of 92.7%. The findings of this study demonstrate the potential of using machine learning methods for the accurate and timely prediction of COVID-19. DenseNet169 outperformed other models and provided better accuracy for the prediction of COVID-19 Binary Classification.

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