Classification of COVID-19 pneumonia from chest CT images based on reconstructed super-resolution images and VGG neural network

Abstract The manual diagnostic tests performed in laboratories for pandemic disease such as COVID19 is time-consuming, requires skills and expertise of the performer to yield accurate results. Moreover, it is very cost ineffective as the cost of test kits is high and also requires well-equipped labs to conduct them. Thus, other means of diagnosing the patients with presence of SARS-COV2 (the virus responsible for COVID19) must be explored. A radiography method like chest CT images is one such means that can be utilized for diagnosis of COVID19. The radio-graphical changes observed in CT images of COVID19 patient helps in developing a deep learning-based method for extraction of graphical features which are then used for automated diagnosis of the disease ahead of laboratory-based testing. The proposed work suggests an Artificial Intelligence (AI) based technique for rapid diagnosis of COVID19 from given volumetric CT images of patient’s chest by extracting its visual features and then using these features in the deep learning module. The proposed convolutional neural network is deployed for classifying the infectious and non-infectious SARS-COV2 subjects. The proposed network utilizes 746 chests scanned CT images of which 349 images belong to COVID19 positive cases while remaining 397 belong negative cases of COVID19. The extensive experiment has been completed with the accuracy of 98.4 %, sensitivity of 98.5 %, the specificity of 98.3 %, the precision of 97.1 %, F1score of 97.8 %. The obtained result shows the outstanding performance for classification of infectious and non-infectious for COVID19 cases.

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Deep Transfer Learning Pipelines with Apache Spark and Keras TensorFlow combined with Logistic Regression to Detect COVID-19 in Chest CT Images

Walailak Journal of Science and Technology (WJST) ◽

10.48048/wjst.2021.13109 ◽

2021 ◽

Vol 18 (11) ◽

Author(s):

Houssam BENBRAHIM ◽

Hanaa HACHIMI ◽

Aouatif AMINE

Keyword(s):

Neural Network ◽

Logistic Regression ◽

Convolutional Neural Network ◽

Transfer Learning ◽

Virus Disease ◽

Ct Images ◽

Chest Ct ◽

Apache Spark ◽

Difficult Situation ◽

Novel Coronavirus

The SARS-CoV-2 (COVID-19) has propagated rapidly around the world, and it became a global pandemic. It has generated a catastrophic effect on public health. Thus, it is crucial to discover positive cases as early as possible to treat touched patients fastly. Chest CT is one of the methods that play a significant role in diagnosing 2019-nCoV acute respiratory disease. The implementation of advanced deep learning techniques combined with radiological imaging can be helpful for the precise detection of the novel coronavirus. It can also be assistive to surmount the difficult situation of the lack of medical skills and specialized doctors in remote regions. This paper presented Deep Transfer Learning Pipelines with Apache Spark and KerasTensorFlow combined with the Logistic Regression algorithm for automatic COVID-19 detection in chest CT images, using Convolutional Neural Network (CNN) based models VGG16, VGG19, and Xception. Our model produced a classification accuracy of 85.64, 84.25, and 82.87 %, respectively, for VGG16, VGG19, and Xception. HIGHLIGHTS Deep Transfer Learning Pipelines with Apache Spark and Keras TensorFlow combined with Logistic Regression using CT images to screen for Corona Virus Disease (COVID-19) Automatic detection of COVID-19 in chest CT images Convolutional Neural Network (CNN) based models VGG16, VGG19, and Xception to predict COVID-19 in Computed Tomography image GRAPHICAL ABSTRACT

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