Automated evaluation of Tuberculosis using Deep Neural Networks

Abstract

INTRODUCTION: Tuberculosis (TB) is a chronic, progressive infection that often has a latent period after the initial infection period. Early awareness from those period to have better prevention steps becomes an indispensable part for patients who want to lengthen their lives. Hence, applying cutting-edge technologies to support the medical business domain plays a key role in improving speed and accuracy in methods of diagnosis. Deep Neural Network-based technique (DNN) is one of such methods which offers positive results by leveraging the advantages of analyzing deeply the data, especially image data format via tons of deep layers of the neural networks. Our study was wrapped up by objectively assessing the performance of modern Deep Neural Network approaches and suggesting a model offering good results in terms of the selected metrics as defined later. In order to achieve optimized results, the chosen model must adapt well to the datasets but require less hardware and computational resources. OBJECTIVES: Our objective is to pick up and train a Deep Neural Network architecture which is highly trusted and flexibly fitted and applied to various datasets with minimum configurations. This will be used to produce good predictions based on the input data which are Chest X-ray images retrieved from the published datasets. METHODS: We have been approaching this problem by using the recognized datasets which have already been published before, then resizing them to the consistent input data for training purposes. In terms of Deep Neural Networks, we picked up VGG16 as the baseline network architecture, then use other ones which are state-of-the-art networks for comparison purposes. After all, we recommend the neural network architecture offering the most positive results based on accuracy and recall measurements. So that, this network architecture will show flexibility when fitting into diverse datasets representing different areas in the world that suffered from Tuberculosis before. RESULTS: After conducting the experiments, we observed that the Mobilenet model produced great results based on the predefined metrics for most of the proposed datasets. It shows the versatility which is applicable to all CXR datasets, especially for the Tuberculosis ones. CONCLUSION: Tuberculosis is still one of the most dangerous illnesses in the world that needs vital methods to prevent and detect soon so that patients are able to keep their lives longer. After this research, we are constantly improving the current accuracy of the models and applying the current results of this research for later problems such as detecting the Tuberculosis areas in real-time and supporting doctors to make decisions based on the current status of patients.