Comparison of Machine Learning Performance for Covid-19 X-ray Image Classification Based on Texture Features

The most prevalent method for early detection of Covid-19 is polymerase chain reaction (PCR). Unfortunately, the quantity of accessible test kits restricts the use of PCR. The development of automatic detection is limited due to the absence of the digital output of PCR data, resulting in an extremely low sensitivity level. Another possibility for Covid-19 detection is based on medical imaging diagnostic. Using digital images offers the opportunity to develop a computer-based system. Image processing mixed with machine learning is the purpose of this study. The comparison of machine learning performance aimed to determine the best classification model. The methods developed for the Covid-19 detection system applied 2-D Haar Wavelet Transform feature extraction and classification methods of Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Decision Tree (DT). Quadratic SVM achieved the best classification results with an accuracy of 86.96%, precision of 94.64%, recall of 86.89%, specificity of 90.00%, and F-score of 89.83%. This study succeeded in comparing three machine learning methods with texture features.