Deep Learning Algorithm for Detection of Tuberculosis with Digital X-ray Images

Information Science and Engineering

This introduction delves into the application of deep learning methodologies for interpreting thoracic X-ray images to detect tuberculosis (TB), addressing the significant global health issue posed by the disease. Advanced diagnostic tools are essential for the early and accurate identification of TB. Researchers have leveraged the capabilities of deep learning in image analysis to develop innovative approaches that enhance TB detection through the automated interpretation of thoracic X-rays. This comprehensive survey provides insights into recent advancements, methodologies used, and the challenges encountered at the intersection of medical imaging and TB detection strategies. By synthesizing existing literature, it not only highlights achievements but also identifies gaps in our understanding, paving the way for future breakthroughs in this critical field. The survey traces the evolution of deep learning techniques in TB detection, from traditional diagnostic methods to the incorporation of state-of-the-art artificial intelligence algorithms. It examines how these methodologies have transformed the interpretation of thoracic X-rays, enabling automated detection and analysis with unprecedented accuracy and efficiency. Furthermore, the review highlights the diverse range of deep learning architectures and algorithms employed in TB detection, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and their variants. It explores the complexities of model training, data preprocessing, and feature extraction techniques tailored to thoracic X-ray images, offering valuable insights into the technical nuances of using deep learning for TB detection.

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