Open AccessEfficient intelligent system for diagnosis pneumonia (SARS-COVID19) in X-Ray images empowered with initial clustering
Author(s) -
Shadab Ali,
Ali Hakem Alsaeedi,
Dhiah AlShammary,
H. H. Alsaeedi,
Hoorish Abid
Publication year - 2021
Publication title -
indonesian journal of electrical engineering and computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.241
H-Index - 17
eISSN - 2502-4760
pISSN - 2502-4752
DOI - 10.11591/ijeecs.v22.i1.pp241-251
Subject(s) - cluster analysis , artificial intelligence , histogram , computer science , pattern recognition (psychology) , covid-19 , process (computing) , cluster (spacecraft) , machine learning , data mining , image (mathematics) , medicine , infectious disease (medical specialty) , programming language , operating system , disease , pathology
This paper proposes efficient models to help diagnose respiratory (SARS-COVID19) infections by developing new data descriptors for standard machine learning algorithms using X-Ray images. As COVID-19 is a significantly serious respiratory infection that might lead to losing life, artificial intelligence plays a main role through machine learning algorithms in developing new potential data classification. Data clustering by K-Means is applied in the proposed system advanced to the training process to cluster input records into two clusters with high harmony. Principle Component Analysis PCA, histogram of orientated gradients (HOG) and hybrid PCA and HOG are developed as potential data descriptors. The wrapper model is proposed for detecting the optimal features and applied on both clusters individually. This paper proposes new preprocessed X-Ray images for dataset featurization by PCA and HOG to effectively extract X-Ray image features. The proposed systems have potentially empowered machine learning algorithms to diagnose Pneumonia (SARS-COVID19) with accuracy up to %97.