Enhancing Multiclass Pneumonia Classification with Machine Learning and Textural Features

• Autorzy: Godbin A. Beena , Jasmine S. Graceline

Identyfikatory

DOI

10.22630/MGV.2023.32.3.5

• Języki publikacji: EN

Abstrakty

EN

The highly infectious and mutating COVID-19, known as the novel coronavirus, poses a substantial threat to both human health and the global economy. Detecting COVID-19 early presents a challenge due to its resemblance to pneumonia. However, distinguishing between the two is critical for saving lives. Chest X-rays, empowered by machine learning classifiers and ensembles, prove effective in identifying multiclass pneumonia in the lungs, leveraging textural characteristics such as GLCM and GLRLM. These textural features are instilled into the classifiers and ensembles within the domain of machine learning. This article explores the multiclass categorization of X-ray images across four categories: COVID-19-impacted, bacterial pneumonia-affected, viral pneumonia-affected, and normal lungs. The classification employs Random Forest, Support Vector Machine, K-Nearest Neighbor, LGBM, and XGBoost. Random Forest and LGBM achieve an impressive accuracy of 92.4% in identifying GLCM features. The network’s performance is evaluated based on accuracy, precision, sensitivity and F1-score.

Słowa kluczowe

EN

COVID-19; chest X-ray; feature extraction; GLCM; GLRLM; machine learning; random; forest; XGB; SVM

• Wydawca: Institute of Information Technology of the Warsaw University of Life Sciences – SGGW
• Czasopismo: Machine Graphics and Vision
• Rocznik: 2023
• Tom: Vol. 32, No. 3/4
• Strony: 83--106
• Opis fizyczny: Bibliogr. 39 poz., il., rys., wykr.

Twórcy

autor

Godbin A. Beena

• School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India

• https://orcid.org/0000-0001-8668-0526

autor

Jasmine S. Graceline

• School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India

• https://orcid.org/0000-0001-6267-2433

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