Pneumonia identification based on lung texture analysis using modified k-nearest neighbour

Abstract

Covid-19 is a virus that was first discovered in China, which has the impact of mild and severe respiratory infections such as pneumonia. Pneumonia is inflammation and consolidation of lung tissue due to infectious agents. Generally pneumonia has a high mortality rate, as do Covid-19 patients. For now, it is very difficult to distinguish between Pneumonia and Covid-19, due to the high similarity of X-Ray image results. The high similarity has an impact on the difficulty of difference between Pneumonia and Covid-19 patients. This research aims to be able to different Pneumonia and Covid-19 patients based on texture analysis of the Gray Level Co-Occurrence Matrix using Modified k-Nearest Neighbour as a classifier. The calculations used in the Gray Level Co-Occurrence Matrix method are Contrast, Correlation, Energy, and Homogeneity which will be input for the Modified k-Nearest Neighbour classifier. The results showed that the highest accuracy is when the value of K = 3 using Manhattan Distance and 80%:20% data percentage, which is 87.5%. For the values of K = 7 and K = 9 there is no change in accuracy, so it can be concluded that the value of K that affects accuracy only occurs at the values of K = 3 and K = 5. Then, the higher the K value, the lower the resulting accuracy.