ABSTRACT
According to global Tuberculosis report 2024 of World Health Organization (WHO), an estimated 10.8 million fell ill with Tuberculosis (TB) worldwide. To address these challenges, the study on Multimodal approach for early detection of Tuberculosis using Deep Learning, aims to develop an advanced detection system using enhanced multimodal deep learning algorithms. The proposed system will integrate medical imaging, such as chest radiographs and clinical records of symptoms to improve diagnostic accuracy and speed. The use of enhanced hybrid deep learning algorithms ensures continuous learning and adaptation, maintaining the systems effectiveness as new data becomes available. This dynamic approach enables quicker diagnosis and treatment initiation, decreasing the overall burden of Tuberculosis on individuals and society.
