ICTACT Journal on Image and Video Processing

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

COV-CT-NET - A Deep Learning Model for COVID-19, Community-Acquired Pneumonia Detection Using CT Images


Affiliations
1 Department of Computer Science and Engineering, Government College of Engineering and Textile Technology, Berhampore, India
2 Department of Computer Science and Engineering, Murshidabad College of Engineering and Technology, India
3 Department of Computer Science and Engineering, Government College of Engineering and Leather Technology, Kolkata, India
4 Department of Computer Science and Engineering, Jadavpur University, India
     

   Subscribe/Renew Journal


The world has witnessed the deadly impact of the Novel Corona Virus (COVID-19), claiming millions of lives since its outbreak in early December 2019. Early virus detection plays a crucial role in controlling this highly contagious disease. Though Reverse Transcription Polymerase Chain Reaction (RT-PCR) is the current standard for confirmation of COVID-19, it is time-consuming. Computed Tomography (CT) imaging of the lungs can preferably be used for fast diagnosis of the disease as it is more sensitive and can detect complications. Due to the unavailability of adequate expertise, a deep learning-based model on CT images is a potential solution for fast detecting SARS Cov2 virus. In this study, we developed a simple but robust Convolution Neural Network model with multiclass detection ability between normal lungs, COVID-19 infected lungs and any other Community-Acquired Pneumonia (CAP) infection using Chest CT images. It is tested on a publicly available dataset, COVID-CT-MD and it achieved slice level accuracy of 99% on test dataset. We also attempted slice-level prediction of the unlabelled slices available in the dataset of COVID-19 and CAP cases.

Keywords

COVID-19 Detection, CAP, CT Imaging, CNN, Deep Learning
Subscription Login to verify subscription
User
Notifications
Font Size

  • World Health Organization (WHO), “WHO Coronavirus (COVID-19) Dashboard”, Available at https://www.covid19.who.int, Accessed on 2023.

  • X. Xu and G. Lang, “A Deep Learning System to Screen Novel Coronavirus Disease 2019 Pneumonia”, Engineering, Vol. 6, No. 10, pp. 1122-1129, 2020.

  • M.V.M.K Atalla and I.A. Moonesar, “Detection of COVID-19 using Deep Learning Techniques and Cost Effectiveness Evaluation: A survey”, Frontiers in Artificial Intelligence, Vol. 5, pp. 1-13, 2022.

  • Y. Fang P. Pang and W. Ji, “Sensitivity of Chest CT for COVID-19: Comparison to RT-PCR”, Radiology, Vol. 296, No. 2, pp. 115-117, 2020.

  • H.Y. Wong, K.W. Chiu, T.W. Chung and E.Y. Lee, “Frequency and Distribution of Chest Radiographic Findings in Patients Positive for COVID-19”, Radiology, Vol. 296, No. 2, pp. 72-78, 2020.

  • A. Borakati, A. Perera and T. Sood, “Diagnostic Accuracy of X-ray Versus CT in COVID-19: A Propensity-Matched Database Study”, BMJ Open, Vol. 10, No. 11, pp. 42946-42953, 2020.

  • M. Chung and A. Jacobi, “CT Imaging Features of 2019 Novel Corona Virus (2019-nCoV)”, Radiology, Vol. 295, No. 1, pp. 202-207, 2020.

  • N. Yu, S. Cai and Y. Guo, “Lung Involvement in Patients with Corona Virus Disease-19 (COVID-19): A Retrospective Study based on Quantitative CT Findings”, Chinese Journal of Academic Radiology, Vol. 3, pp. 102-107, 2020.

  • S. Inui and Y. Uwabe, “Chest CT Findings in Cases from the Cruise ship Diamond Princess with Coronavirus Disease (COVID-19)”, Radiology: Cardiothoracic Imaging, Vol. 2, No. 2, pp. 200110-200123, 2020.

  • M.Y. Ng and C.K. Hui, “Imaging Profile of the COVID-19 Infection: Radiologic Findings and Literature Review”, Radiology: Cardiothoracic Imaging, Vol. 2, No. 1, pp. 34-43, 2020.

  • H. Shi, Y. Fan and C. Zheng, “Radiological Findings from 81 patients with COVID-19 Pneumonia in Wuhan, China: a Descriptive Study”, The Lancet Infectious Diseases, Vol. 20, No. 4, pp. 425-434, 2020.

  • C. Janiesch, P. Zschech and K. Heinrich, “Machine Learning and Deep Learning”, Electronic Markets, Vol. 31, No. 3, pp. 685-695, 2021.

  • G. Carneiro and L. Yang, “Review of Deep Learning Methods in Mammography, Cardiovascular, and Microscopy Image Analysis”, Proceedings of International Conference on Deep Learning and Convolutional Neural Networks for Medical Image Computing, pp. 11-32, 2017.

  • G. Litjens and C.I. Sanchez, “A Survey on Deep Learning in Medical Image Analysis”, Medical Image Analysis, Vol. 42, pp. 60-88, 2017.

  • R. Yamashita and K. Togashi, “Convolutional Neural Networks: An Overview and Application in Radiology”, Insights into Imaging, Vol. 9, pp. 611-629, 2018.

  • M.V. Herk, “Errors and Margins in Radiotherapy”, in Proceeding of Seminar in Radiation Oncology, WB Saunders, Vol. 14, No. 1, pp. 52-64, 2004.

  • S. Yang, L. Jiang, Z. Cao and F. Shan, “Deep Learning for Detecting Corona Virus Disease 2019 (COVID-19) on High-Resolution Computed Tomography: A Pilot Study”, Annals of Translational Medicine, Vol. 8, No. 7, pp. 1-12, 2020.

  • L. Wang and A. Wong, “Covid-Net: A Tailored Deep Convolutional Neural Network Design for Detection of Covid-19 Cases from Chest X-Ray Images”, Scientific Reports, Vol. 10, No. 1, pp. 1-12, 2020.

  • S. Hu and H. Ye, “Weakly Supervised Deep Learning for Covid-19 Infection Detection and Classification from CT Images”, IEEE Access, Vol. 8, pp. 118869-118883, 2020.

  • T. Mahmud and S.A. Fattah, “CovXNet: A Multi-Dilation Convolutional Neural Network for Automatic COVID-19 and other Pneumonia Detection from Chest X-Ray Images with Transferable Multi-Receptive Feature Optimization”, Computers in Biology and Medicine, Vol. 122, pp. 103869-103877, 2020.

  • M. Rahimzadeh and S.M. Sakhaei, “A Fully Automated Deep Learning-Based Network for Detecting COVID-19 from a New and Large Lung CT Scan Dataset”, Biomedical Signal Processing and Control, Vol. 68, pp. 102588-102595, 2021.

  • S. Heidarian, S.F. Atashzar, A. Oikonomou and A. Mohammadi, “COVID-FACT: A Fully-Automated Capsule Network-Based Framework for Identification of COVID-19 Cases from Chest CT Scans”, Frontiers in Artificial Intelligence, Vol. 4, pp. 598932-598943, 2021.

  • P. Afshar and A. Mohammadi, “COVID-CT-MD, COVID-19 Computed Tomography Scan Dataset Applicable in Machine Learning and Deep Learning”, Scientific Data, Vol. 8, No. 1, pp. 121, 2021.

  • A. Khadidos and G. Tsaramirsis, “Analysis of Covid-19 Infections on a CT Image using Deepsense Model”, Frontiers in Public Health, Vol. 8, pp. 599550-599562, 2020.

  • M. Grandini, E. Bagli and G. Visani, “Metrics for Multi-Class Classification: An Overview”, Proceedings of International Conference on Deep Learning, pp. 5756-5764, 2020.

  • N. Yu and Y. Guo, “Lung Involvement in Patients with Coronavirus Disease-19 (COVID-19): A Retrospective Study based on Quantitative CT Findings”, Chinese Journal of Academic Radiology, Vol. 3, pp. 102-107, 2020.

  • F.S. Nahm, “Receiver Operating Characteristic Curve: Overview and Practical use for Clinicians”, Korean journal of Anesthesiology, Vol. 75, No. 1, pp. 25-36, 2022.


Abstract Views: 49

PDF Views: 1




  • COV-CT-NET - A Deep Learning Model for COVID-19, Community-Acquired Pneumonia Detection Using CT Images

Abstract Views: 49  |  PDF Views: 1

Authors

Abul Hasnat
Department of Computer Science and Engineering, Government College of Engineering and Textile Technology, Berhampore, India
Mangalmay Das
Department of Computer Science and Engineering, Murshidabad College of Engineering and Technology, India
Santanu Halder
Department of Computer Science and Engineering, Government College of Engineering and Leather Technology, Kolkata, India
Debotosh Bhattacharjee
Department of Computer Science and Engineering, Jadavpur University, India

Abstract


The world has witnessed the deadly impact of the Novel Corona Virus (COVID-19), claiming millions of lives since its outbreak in early December 2019. Early virus detection plays a crucial role in controlling this highly contagious disease. Though Reverse Transcription Polymerase Chain Reaction (RT-PCR) is the current standard for confirmation of COVID-19, it is time-consuming. Computed Tomography (CT) imaging of the lungs can preferably be used for fast diagnosis of the disease as it is more sensitive and can detect complications. Due to the unavailability of adequate expertise, a deep learning-based model on CT images is a potential solution for fast detecting SARS Cov2 virus. In this study, we developed a simple but robust Convolution Neural Network model with multiclass detection ability between normal lungs, COVID-19 infected lungs and any other Community-Acquired Pneumonia (CAP) infection using Chest CT images. It is tested on a publicly available dataset, COVID-CT-MD and it achieved slice level accuracy of 99% on test dataset. We also attempted slice-level prediction of the unlabelled slices available in the dataset of COVID-19 and CAP cases.

Keywords


COVID-19 Detection, CAP, CT Imaging, CNN, Deep Learning

References