A hybrid architecture of DenseNet201 and XGBoost to detect tuberculosis from chest x-ray

Deep neural networks are frequently used to automate the examination of radiographic images in medical. These approaches may be used to train on huge datasets or extract features from small datasets using pre-trained networks. Due to the lack of large pulmonary tuberculosis datasets, it is possible to diagnose tuberculosis using pre-trained deep convolutional neural networks. Thus, this article aims to detect and diagnose tuberculosis in chest X-rays by combining a pre-trained deep convolutional neural network with a machine learning model. Combined the deep pre-trained DenseNet201 network with the machine learning XGBoost classifier to create a hybrid model for classifying patients as tuberculosis infected or not. The proposed model extracts feature using the pre-trained DenseNet201 neural networks and classify them employing the XGBoost classifier. We performed extensive experiments to assess the performance of the proposed DenseNet201-XGBoost model using tuberculosis chest x-ray images. Comparative study shows that the proposed DenseNet201-XGBoost-based tuberculosis classification model outperforms other competing approaches.