CoviNet: Covid-19 diagnosis using machine learning analyses for computerized tomography images

The Covid-19 is a highly contagious and virulent disease caused by the Severe Acute Respiratory Syndrome - Corona Virus – 2 (SARS-CoV-2). Over 146 million cases and 3.1 million deaths were reported worldwide as of April 27, 2021. A multinational consensus from the Fleischner Society reported that Computerized Tomography (CT) can be utilized for the early diagnosis of Covid-19. However, this classification involves radiologists’ time and efforts significantly. It is crucial to develop an automated analysis of CT images to save their time and efforts. In this paper, we propose CoviNet, a deep three-dimensional convolutional neural network (3D-CNN) to diagnose Covid-19 from CT images. We trained and tested the proposed CoviNet using two public datasets with radiologist-labeled CT images. The experimental results show the proposed CoviNet is promising.

[1]  Steve B. Jiang,et al.  A deep convolutional neural network-based automatic delineation strategy for multiple brain metastases stereotactic radiosurgery , 2017, PloS one.

[2]  S. Jassim,et al.  Machine Learning Analysis of Chest CT Scan Images as a Complementary Digital Test of Coronavirus (COVID-19) Patients , 2020, medRxiv.

[3]  A. P. Gonchar,et al.  MosMedData: data set of 1110 chest CT scans performed during the COVID-19 epidemic , 2020 .

[4]  Yuan Gao,et al.  Weakly Supervised Deep Learning for COVID-19 Infection Detection and Classification From CT Images , 2020, IEEE Access.

[5]  Ling Shao,et al.  Inf-Net: Automatic COVID-19 Lung Infection Segmentation From CT Images , 2020, IEEE Transactions on Medical Imaging.

[6]  Chenbin Liu,et al.  Differentiating novel coronavirus pneumonia from general pneumonia based on machine learning , 2020, Biomedical engineering online.

[7]  Kaijin Xu,et al.  A Deep Learning System to Screen Novel Coronavirus Disease 2019 Pneumonia , 2020, Engineering.

[8]  Evgeny Burnaev,et al.  3D Deformable Convolutions for MRI Classification , 2019, 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA).

[9]  T. Egglin,et al.  Performance of radiologists in differentiating COVID-19 from viral pneumonia on chest CT , 2020, Radiology.

[10]  Afshin Mohammadi,et al.  COVIDiag: a clinical CAD system to diagnose COVID-19 pneumonia based on CT findings , 2020, European Radiology.

[11]  Yitian Zhao,et al.  Introducing the GEV Activation Function for Highly Unbalanced Data to Develop COVID-19 Diagnostic Models , 2020, IEEE Journal of Biomedical and Health Informatics.

[12]  Xin Yang,et al.  CT radiomics can help screen the Coronavirus disease 2019 (COVID-19): a preliminary study , 2020, Science China Information Sciences.

[13]  Pedro Silva,et al.  COVID-19 detection in CT images with deep learning: A voting-based scheme and cross-datasets analysis , 2020, Informatics in Medicine Unlocked.

[14]  K. Yeom,et al.  End-to-end automatic differentiation of the coronavirus disease 2019 (COVID-19) from viral pneumonia based on chest CT , 2020, European Journal of Nuclear Medicine and Molecular Imaging.

[15]  P. Xie,et al.  COVID-CT-Dataset: A CT Scan Dataset about COVID-19 , 2020, ArXiv.

[16]  H. Kauczor,et al.  The Role of Chest Imaging in Patient Management during the COVID-19 Pandemic: A Multinational Consensus Statement from the Fleischner Society , 2020, Radiology.

[17]  Yoshua Bengio,et al.  Convolutional networks for images, speech, and time series , 1998 .

[18]  Abbas Sharifi,et al.  Diagnosis and detection of infected tissue of COVID-19 patients based on lung x-ray image using convolutional neural network approaches , 2020, Chaos, Solitons & Fractals.

[19]  M. Punjabi,et al.  Diagnosis of COVID-19 using CT scan images and deep learning techniques , 2020, Emergency Radiology.

[20]  D. Chicco,et al.  The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation , 2020, BMC Genomics.

[21]  Youngbin Shin,et al.  COVID-19 Pneumonia Diagnosis Using a Simple 2D Deep Learning Framework With a Single Chest CT Image: Model Development and Validation , 2020, Journal of medical Internet research.

[22]  Manoranjan Paul,et al.  COVID-19 Detection Through Transfer Learning Using Multimodal Imaging Data , 2020, IEEE Access.

[23]  Anvar Kurmukov,et al.  CT-based COVID-19 Triage: Deep Multitask Learning Improves Joint Identification and Severity Quantification , 2020, ArXiv.

[24]  Yaozong Gao,et al.  Adaptive Feature Selection Guided Deep Forest for COVID-19 Classification With Chest CT , 2020, IEEE Journal of Biomedical and Health Informatics.

[25]  D. Kisku,et al.  COVID-19 Detection on Chest X-Ray and CT Scan Images Using Multi-image Augmented Deep Learning Model , 2020, bioRxiv.

[26]  Varalakshmi Perumal,et al.  Detection of COVID-19 using CXR and CT images using Transfer Learning and Haralick features , 2020, Applied Intelligence.