Pre-processing Effects of the Tuberculosis Chest X-Ray Images on Pre-trained CNNs: An Investigation

Tuberculosis (TB) is a serious infectious disease which is one of the top causes of death worldwide. In 2017, 1.6 million people died from the disease according to the World Health Organization (WHO). The earlier identification and treatment of the TB is critical for preventing death and decreasing risk of transmitting the disease to others. Computer-aided diagnosis (CADx) systems are essential tools to speed up the decision-making process of experts and provide more efficient, accurate and systematic solutions. Chest radiography (CXR) is one of the most common and effective imaging technique for the detection of thoracic diseases such as TB and lung cancer. In this study, three different region of interests (ROIs) based pre-processing methods are applied to two CXR image datasets (namely, Montgomery and Shenzhen). We used three pre-trained convolutional neural networks (CNNs) (namely, AlexNet, VGG16, VGG19) as deep learning models and deep feature extractors for automatic classification of TB disease. We investigate the pre-processing effects of TB CXR images on the classifier whether ROI is selected and remaining regions of images are set pixel values to white, black and same pixel values in the original images. Experimental results indicate that proposed methods contribute to the classifier performance gain considerably in terms of accuracy rate.