ICTACT Journal on Image and Video Processing

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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
     

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

Abstract Views: 161  |  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