A Lung Disease Classification Based on Feature Fusion Convolutional Neural Network with X-ray Image Enhancement

With the explosive growth of lung diseases in patients, automatically detecting diseases and obtaining accurate diagnosis through the X-ray medical images become the new research focus in the field of computer science and artificial intelligence to save the significant cost of manual labeling and classifying. However, the quality of common radiograph is not satisfied for the most tasks, and traditional methods are deficient to deal with the massive images. Therefore, we present a feature fusion convolutional neural network (CNN) model to detect pneumothorax from chest X-ray images. Firstly, the preprocessed image samples are enhanced by two methods. Then, a feature fusion CNN model is introduced to combine the Gabor features with the enhanced information extracted from the images and implement the final classification. Comprehensive qualitative and quantitative experiments demonstrate that our proposed model achieve better results in multi-angle views.