DKCNN: Improving deep kernel convolutional neural network-based COVID-19 identification from CT images of the chest.

Journal: Journal of X-ray science and technology

PMID: 38820059

BACKGROUND: An efficient deep convolutional neural network (DeepCNN) is proposed in this article for the classification of Covid-19 disease.

T Vaikunta Pai

Department of Information Science and Engineering, NMAM Institute of Technology-Affiliated to NITTE (Deemed to be University), Bangalore, Karnataka, India.
K Maithili

Department of Computer Science and Engineering (Ai & ML), KG Reddy College of Engineering and Technology, Hyderabad, Telangana, India.
Ravula Arun Kumar

Department of Computer Science and Engineering, Vardhaman College of Engineering, Hyderabad, Telangana, India.
D Nagaraju

Department of Computer Science and Engineering, Sri Venkatesa Perumal College of Engineering and Technology, Puttur, Andhra Pradesh, India.
D Anuradha

Department of Computer Science and Business Systems, Panimalar Engineering College, Chennai, India.
Shailendra Kumar

Department of Electronics and Communication Engineering, Integral University Lucknow, Uttar Pradesh, India.
Ananda Ravuri

Intel Corporation, Hillsboro, OR, USA.
T Sunilkumar Reddy

Department of Computer Science and Engineering, Sri Venkatesa Perumal College of Engineering and Technology, Puttur, Andhra Pradesh, India.
M Sivaram

Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha Nagar, Thandalam, Tamil Nadu, India.
R G Vidhya

Department of ECE, HKBKCE, Bangalore, India.

Algorithms Betacoronavirus Coronavirus Infections COVID-19 Deep Learning Humans Image Processing, Computer-Assisted Neural Networks, Computer Pandemics Pneumonia, Viral SARS-CoV-2 Thorax Tomography, X-Ray Computed

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