Please use this identifier to cite or link to this item:https://hdl.handle.net/20.500.12259/145298
Type of publication: research article
Type of publication (PDB): Straipsnis Clarivate Analytics Web of Science / Article in Clarivate Analytics Web of Science (S1)
Field of Science: Informatika / Informatics (N009)
Author(s): Akram, Tallha;Attique, Muhammad;Gul, Salma;Shahzad, Aamir;Altaf, Muhammad;Naqvi, S. Syed Rameez;Damaševičius, Robertas;Maskeliūnas, Rytis
Title: A novel framework for rapid diagnosis of COVID‑19 on computed tomography scans
Is part of: Pattern Analysis and Applications New York : Springer Nature, 2021, Vol. 24, iss. 3
Extent: p. 951-964
Date: 2021
Keywords: Covid19;Features extraction;Features selection;Features classifcation
Abstract: Since the emergence of COVID-19, thousands of people undergo chest X-ray and computed tomography scan for its screening on everyday basis. This has increased the workload on radiologists, and a number of cases are in backlog. This is not only the case for COVID-19, but for the other abnormalities needing radiological diagnosis as well. In this work, we present an automated technique for rapid diagnosis of COVID-19 on computed tomography images. The proposed technique consists of four primary steps: (1) data collection and normalization, (2) extraction of the relevant features, (3) selection of the most optimal features and (4) feature classifcation. In the data collection step, we collect data for several patients from a public domain website, and perform preprocessing, which includes image resizing. In the successive step, we apply discrete wavelet transform and extended segmentation-based fractal texture analysis methods for extracting the relevant features. This is followed by application of an entropy controlled genetic algorithm for selection of the best features from each feature type, which are combined using a serial approach. In the fnal phase, the best features are subjected to various classifers for the diagnosis. The proposed framework, when augmented with the Naive Bayes classifer, yields the best accuracy of 92.6%. The simulation results are supported by a detailed statistical analysis as a proof of concept
Internet: https://www.vdu.lt/cris/bitstream/20.500.12259/145298/1/ISSN1433-7541_2021_V_24_3.PG_951-964.pdf
https://hdl.handle.net/20.500.12259/145298
https://doi.org/10.1007/s10044-020-00950-0
Affiliation(s): Taikomosios informatikos katedra
Vytauto Didžiojo universitetas
Appears in Collections:1. Straipsniai / Articles
Universiteto mokslo publikacijos / University Research Publications

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