Using a Convolutional Neural Network as Feature Extractor for Different Machine Learning Classifiers to Diagnose Pneumonia

Abstract

Pneumonia is a general public health problem. It is an important risk factor, especially for children under 5 years old and people aged 65 and older. Fortunately, it is a treatable disease when diagnosed in the early phase. The most common diagnostic method known for the disease is chest X-Rays. However, the disease can be confused with different disorders in the lungs or its variants by experts. In this context, computer-aided diagnostic systems are necessary to provide a second opinion to experts. Convolutional neural networks are a subfield in deep learning and they have demonstrated success in solving many medical problems. In this paper, Xception which is a convolutional neural network was trained with the transfer learning method to detect viral pneumonia, normal cases, and bacterial pneumonia in chest X-Rays. Then, five different machine learning classification algorithms were trained with the features obtained by the trained convolutional neural network. The classification performances of the algorithms were compared. According to the test results, Xception achieved the best classification result with an accuracy of 89.74%. On the other hand, SVM achieved the closest classification performance to the convolutional neural network model with 89.58% accuracy.

Keywords

• Artificial Intelligence
• Pneumonia
• Convolutional Neural Networks
• Deep Learning
• Machine Learning

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