Enhancing Tuberculosis Detection in Chest X-Ray Images Using ResNet Models
Keywords:
Tuberculosis (TB), Deep Learning, ResNet-18, ResNet-50 ,ResNet-101Abstract
Accurate diagnosis of Tuberculosis (TB) from chest X-ray (CXR) images is essential for effective medical intervention. This study explores the application of deep learning models, specifically ResNet-18, ResNet-50, and ResNet-101, in automating TB detection and classification. Leveraging a diverse dataset comprising 3500 CXR images categorized as "Normal" and 700 as "TB-affected," this research investigates the efficacy of ResNet models in feature extraction and classification. The experimental setup involved training and evaluation of the models using standard metrics such as accuracy and precision. The findings demonstrate notable improvements in accuracy and precision, with ResNet-101 emerging as the top performer, achieving better accuracy. These results highlight the potential of advanced neural network architectures in revolutionizing TB diagnosis and healthcare outcomes. Further details about the dataset, experimental methodology, and specific performance metrics are discussed in detail in the full paper.
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