Covid-19 Detecting in Computed Tomography Lungs Images using Machine and transfer Learning

Abstract

Coronavirus disease 2019 (COVID-19) is a fast-spreading disease infectious that causes lung pneumonia which killed millions of lives around the world and has a significant impact on public healthcare. The diagnostic approach of the infection is principally divided into two broad categories, a laboratory-based and chest radiography approach where the CT imaging tests showed some advantages in the prediction over the other methods. Due to the restricted medical capability and the impressive raise of the suspected cases, the need for finding an immediate, accurate and automated method to alleviate the overcapacity of radiologists’ efforts for diagnosis has emerged . In order to accomplish this objective, our work is based on developing machine and deep learning algorithms to classify chest CT scans into Covid or non-Covid classes. To obtain a good performance, the accuracy of the classifier should be high so the patients may have a clear idea about their state. For this purpose, there are many hyper parameters that can be changed in order to advance the performance of the artificial models that are used for the identification of such illnesses. We have worked on two non-similar datasets from different sources, a small one of 746 images and a larger one with 14486 images. In the other hand, we have proposed various machine learning models starting by an SVM which contains different kernel types, KNN model with changing the distance measurements and an RF model with two different number of trees. Moreover, two CNN based approaches have been developed considering one convolution layer followed by a pooling layer then two consecutive convolution layers followed by a single pooling layer each time. The machine learning models showed better performance comparing to the CNN on the small dataset. While on the large dataset, CNN outperforms these algorithms. In order to improve performance of the models, transfer learning also have been used in this project where we trained the pre-trained InceptionV3 and ResNet50V2 on the same datasets. Among all the examined classifiers, the ResNet50V2 achieved the best scores with 86.67% accuracy, 93.94% sensitivity, 81% specificity and 86% F1-score on the small dataset while the respective scores on the large dataset were 97.52%, 97.28%, 97.77% and 98%. Experimental interpretation advise the potential applicability of ResNet50V2 transfer learning approach in real diagnostic scenarios, which might be of very high usefulness in terms of achieving fast testing for COVID19.