Impact of using various x-ray dataset in detecting tuberculosis based on deep learning

The subject matter is that the characteristics of tuberculosis are difficult to study visually. Therefore, a computer-aided system based on deep learning can be applied to X-ray image recognition. Many studies have been conducted in this area but have yet to achieve a high accuracy rate. The goal of this study is to determine the effect of using various datasets in developing deep learning models. The tasks to be solved include exploring various deep learning architectures and deep fine-tuning hyperparameters, as well as using various dataset sources. The method used is the development of a deep learning model of convolutional neural network (CNN) using transfer learning to classify X-ray images into binary classes of normal and tuberculosis (TB). The CNN architectures used are the pretrained networks of ResNet and EfficientNet, along with their variants. The pre-trained network was trained on a dataset obtained from four sources: Shenzhen, Montgomery, RSNA CXR, and Belarus. The dataset is divided into three schemes: Scheme one consists of the Shenzhen dataset with low-quality X-ray images; Scheme two is the Montgomery, RSNA, and Belarus datasets that show good contrast in the indicated TB area; and Scheme three contains datasets from all sources to allow for more datasets to be learned. The augmentation, dropout, and L2 regularization methods were also applied to enhance learning performance. The following results were obtained: the models performed better with the high-quality X-ray images in Scheme Two but not with the large dataset in Scheme Three. Regarding network performance, the models resulting from ResNet-101 and EfficientNetB0 outperformed the others with good fit learning and capability in recognizing X-ray images with an accuracy rate of 99.2%. In conclusion, the best approach to enhance learning performance is to use high-quality input and apply regularizations.