Abstract: Millions of individuals worldwide are afflicted with the highly contagious infectious disease known as tuberculosis (TB). For the disease to be effectively treated and controlled, early and accurate TB detection is essential. Due to its low cost and wide availability, chest X-ray imaging is frequently used to diagnose tuberculosis. However, it takes skill and can take some time to analyse X-ray images . With the help of X-ray pictures and ResNet, a deep learning model renowned for its outstanding performance in image classification tasks, this study intends to create an automated system for the identification of tuberculosis. The suggested technique makes use of the detailed information seen in chest X-ray pictures to spot TB infection symptoms like the existence of pulmonary lesions, nodules, or cavities.

PDF | DOI: 10.17148/IJARCCE.2023.126105

Open chat
Chat with IJARCCE