About: Development of a Machine-Learning System to Classify Lung CT Scan Images into Normal/COVID-19 Class

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About: Development of a Machine-Learning System to Classify Lung CT Scan Images into Normal/COVID-19 Class Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Development of a Machine-Learning System to Classify Lung CT Scan Images into Normal/COVID-19 Class
Creator	Raja, Madhava Rajinikanth, Venkatesan Kadry, Seifedine Rao, Vaddi Rho, Seungmin »more»
source	ArXiv
abstract	Recently, the lung infection due to Coronavirus Disease (COVID-19) affected a large human group worldwide and the assessment of the infection rate in the lung is essential for treatment planning. This research aims to propose a Machine-Learning-System (MLS) to detect the COVID-19 infection using the CT scan Slices (CTS). This MLS implements a sequence of methods, such as multi-thresholding, image separation using threshold filter, feature-extraction, feature-selection, feature-fusion and classification. The initial part implements the Chaotic-Bat-Algorithm and Kapur's Entropy (CBA+KE) thresholding to enhance the CTS. The threshold filter separates the image into two segments based on a chosen threshold 'Th'. The texture features of these images are extracted, refined and selected using the chosen procedures. Finally, a two-class classifier system is implemented to categorize the chosen CTS (n=500 with a pixel dimension of 512x512x1) into normal/COVID-19 group. In this work, the classifiers, such as Naive Bayes (NB), k-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF) and Support Vector Machine with linear kernel (SVM) are implemented and the classification task is performed using various feature vectors. The experimental outcome of the SVM with Fused-Feature-Vector (FFV) helped to attain a detection accuracy of 89.80%.
has issue date	2020-04-24(xsd:dateTime)
has license	arxiv
sha1sum (hex)	2e31de7d7a946c9fda6a08e9a28c05ffcd725245
resource representing a document's title	Development of a Machine-Learning System to Classify Lung CT Scan Images into Normal/COVID-19 Class
resource representing a document's body	covid:2e31de7d7a946c9fda6a08e9a28c05ffcd725245#body_text
is schema:about of	named entity 'infection' named entity 'large' named entity 'Coronavirus' named entity 'extracted' named entity 'Images' »more»

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