Diagnosis of Pneumonia on Chest Radiographs Using Machine Learning
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Abstract
This research proposes a method for diagnosing pneumonia on chest radiographs using a variety of machine learning methods for disease analysis. Most pneumonia is caused by a respiratory infection, such as bacteria or viruses, and pneumonia causes pleurisy, a condition in which fluid fills the lungs, making it difficult to breathe. The researchers developed a computer-aided pneumonia diagnosis system for the automatic diagnosis of pneumonia by designing a data group design and using a machine learning model: support vector machine (SVM), K-Nearest Neighbor (KNN), Artificial Neural Network (ANN), Adaptive Boosting (AdaBoost) and Logistic Regression. By this method, the weight-mass binding technique was adopted by using the values of the assigned weights from learning data, obtained scores, matrix assessment guidelines, precision standard, recall, F1 score, and area under the curve to be generated as weight vectors for learning guidance. Learning groups and test results were based on a series of chest radiographs of the dataset for experimental use. In this test, the 1,340 images of normal lungs and 1,704 images of abnormal lungs were divided into data sets for learning, and another group of images used as test data that consisted of 233 images of normal lungs and 390 images of abnormal lungs. Based on the research, the developed program was able to analyze the test images with speed and the discriminatory accuracy results yielded 94.38 percent and 84.9 percent of both base-learned and non-learned data, respectively.
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