A New Approach Method for Multi Classification of Lung Diseases using X-Ray Images

Lung disease is one of the most common diseases in today's society. This lung disease's treatment is frequently postponed. This is usually due to a lack of understanding about proper treatment and a lack of clear information about lung disease. Reading the correct X-ray images, which is usually done by experts who are familiar with these X-rays, is one method of detecting lung disease. However, the results of this diagnosis are dependent on the expert's practice schedule and take a long time. This study aims to classify lung disease images using preprocessing, augmentation, and multimachine learning methods, with the goal of achieving high classification performance accuracy with multi-class lung disease. The classification ExtraTrees was obtained from experimental results with unbalanced datasets using a balancing process with augmentation. Precision, Recall, Fi-Score, and Accuracy are 100% for training and testing data 89% for Precision, 88% for Recall, 87 for Fi-Score, and 85% for Accuracy outperform other machine learning models such as Kneighbors, Support Vector Machine (SVM), and Random Forest in classifying lung diseases. The conclusion from this research is that the machine learning approach can detect several lung diseases using X-ray images.