COVID -19 SCREENING BASED ON SUPERVIED DEEP LEARNING

Abstract

CT-scans images are helpful for detecting COVID-19. In this thesis, we are interested by investigating the performance of deep networks with CT-scan images and with detecting COVID-19. We used Convolution deep networks and we will provide insights on the performance of this deep networks. also, compare the performance of this networks and using ensemble learning we try to combine different models. The proposed approach is three-fold and comprises three stages which are training, training with data augmentation then ensemble learning. The first stage is done to train our models with CT-scan images and see their performance. While the second stage is dedicated to see how models will perform using data augmentation technique on CT-scan images. The last stage is using ensemble learning and combine the results of models to increase the accuracy. We conduct experiment on COVID-19CT dataset. With accuracy as performance metric. Experimental results reveal that model without data augmentation are more performing than models with it. VGG16 model is better performing than ResNet50.