Deep learning based computed tomography image classification of COVID-19 patients

Seethalakshmy, A.; Tamilvizhi, T.; Sowjanya, K. Naga; Bala, Bhoomeshwar

doi:10.47974/JIM-1668

Open Access Research Article

Deep learning based computed tomography image classification of COVID-19 patients

^*A. SeethalakshmyCorresponding authorapseethalakshmy@gmail.comDepartment of Mathematics Saveetha School of Engineering Saveetha Institute of Medical and Technical SciencesChennai, IndiaView full profile → , T. Tamilvizhitamilvizhi.phd.it@gmail.comDepartment of Computer Science and Engineering Panimalar Engineering CollegeDepartment of Computer Science and Engineering Panimalar Engineering College Chennai IndiaChennai, Tamil Nadu, IndiaView full profile → , K. Naga Sowjanyasowjanyakasi1@gmail.comDepartment of Information Technology Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering CollegeDepartment of Information Technology Vel Tech Multi Tech Dr. Rangarajan Dr.Sakunthala Engineering CollegeChennai, Tamil Nadu, IndiaView full profile → , Bhoomeshwar Baladrbhoom08@dtu.edu.etDepartment of Computer Science Debre Tabor UniversityDebra Tabor, Debra Tabor, EthiopiaView full profile →

* Corresponding author · click or hover a name for details

pp. 371–381Vol. 26Issue 3April 2023DOI: 10.47974/JIM-1668XML

Published Online:: 01 Apr 2023
Article type:: Research Article
Language:: EN
Article no.:: JIM-1668
Pages:: 371–381

Abstract

Accurate identification of the newest 2019 coronavirus (COVID-19) disease is required for effective illness behavior and management. To categorize and analyses COVID-19 in the prevalent region, for (COVID-19) detection using medical images, computed tomography (CT) imaging is informative, reliable, and quick. Chest CT images are readily available in nearly all hospitals, making it possible to use them to classify COVID-19 patients early on. When COVID-19 infection spreads quickly, a significant amount of time are required for the chest CT-based COVID-19 categorization. Since medical practitioners have scarce time, a computerized analysis of CT scans is required. In this paper, we construct a classification framework involving the extraction of CT image characteristics and categorization. The framework is separated into training and testing modules, with training the classifier aiding in the development of a model that efficiently classifies CT images as input. Deep convolutional neural network including VGG16, ResNet50, DensNet121, and inspectionResNetV2, variations were utilized as classifiers in the present investigation (DCNN) different machine learning and Statistical modeling techniques to identify COVID-19 infections probability and discover lesion for training.

Keywords

Convolutional neural network CT image classification Deep learning Transfer learning

Subject Classifications

Primary 93A30Secondary 49K15

References

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