Covid19 Identification from Chest X-ray Images using Machine Learning Classifiers with GLCM Features
Thepade, Sudeep D. (Pimpri Chinchwad College of Engineering (Pune, Índia). Computer Engineering Department)
Bang, Shalakha V. (Pimpri Chinchwad College of Engineering (Pune, Índia). Computer Engineering Department)
Chaudhari, Piyush R. (Pimpri Chinchwad College of Engineering (Pune, Índia). Computer Engineering Department)
Dindorkar, Mayuresh R. (Pimpri Chinchwad College of Engineering (Pune, Índia). Computer Engineering Department)

Fecha:	2020
Resumen:	From staying quarantined at home, practicing work from home to moving outside wearing masks and carrying sanitizers, every individual has now become so adaptive to so called 'New Normal' post series of lockdowns across the countries. The situation triggered by novel Coronavirus has changed the behaviour of every individual towards every other living as well as non-living entity. In the Wuhan city of China, multiple cases were reported of pneumonia caused due to unknown reasons. The concerned medical authorities confirmed the cause to be Coronavirus. The symptoms seen in these cases were not much different than those seen in case of pneumonia. Earlier the research has been carried out in the field of pneumonia identification and classification through X-ray images of chest. The difficulty in identifying Covid19 infection at initial stage is due to high resemblance of its symptoms with the infection caused due to pneumonia. Hence it is trivial to well distinguish cases of coronavirus from pneumonia that may help in saving life of patients. The paper uses chest X-ray images to identify Covid19 infection in lungs using machine learning classifiers and ensembles with Gray-Level Cooccurrence Matrix (GLCM) features. The advocated methodology extracts statistical texture features from X-ray images by computing a GLCM for each image. The matrix is computed by considering various stride combinations. These GLCM features are used to train the machine learning classifiers and ensembles. The paper explores both the multiclass classification (X-ray images are classified into one of the three classes namely Covid19 affected, Pneumonia affected and normal lungs) and binary classification (Covid19 affected and other). The dataset used for evaluating performance of the method is open sourced and can be accessed easily. Proposed method being simple and computationally effective achieves noteworthy performance in terms of Accuracy, F-Measure, MCC, PPV and Sensitivity. In sum, the best stride combination of GLCM and ensemble of machine learning classifiers is suggested as vital outcome of the proposed method for effective Covid19 identification from chest X-ray images.
Derechos:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades.
Lengua:	Anglès
Documento:	Article ; recerca ; Versió publicada
Materia:	Coronavirus ; Covid19 ; Chest X-ray ; Texture ; Feature extraction ; Gray-level cooccurrence matrix ; Haralick features ; Machine learning ; Random forest ; Logistic ; Multiple layer perceptron ; Ensemble
Publicado en:	ELCVIA : Electronic Letters on Computer Vision and Image Analysis, Vol. 19 Núm. 3 (2020) , p. 85-97 (Regular Issue) , ISSN 1577-5097

Adreça original: https://elcvia.cvc.uab.es/article/view/v19-n3-thepade-bang-chaudari
Adreça alternativa: https://raco.cat/index.php/ELCVIA/article/view/375822
DOI: 10.5565/rev/elcvia.1277

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