Conventional Machine Learning based on Feature Engineering for Detecting Pneumonia from Chest X-rays

Chest X-ray is the standard approach used to diagnose pneumonia and other chest diseases. Early diagnosis of the disease is very relevant in the life of people, but analyzing X-ray images can be complicated and needs the competence of a radiographer. In this paper, we demonstrate the potential of detecting the disease in chest X-rays using conventional machine learning classifiers. The principal component analysis is used for the data dimensionality reduction and features extraction then the extracted features are used to train several model classifiers. We obtained an accuracy of , using of the principal explained variance.

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